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Machine vision-based planar grasping detection is challenging due to uncertainty about object shape, pose, size, etc. Previous methods mostly focus on predicting discrete gripper configurations, and may miss some ground-truth grasp postures. In this article, a pixel-level grasp detection method is proposed, which uses deep neural network to predict pixel-level gripper configurations on RGB images. First, a novel oriented arrow representation model (OAR-model) is introduced to represent the gripper configuration of parallel-jaw and three-fingered gripper, which can partly improve the applicability to different grippers. Then, the adaptive grasping attribute model is proposed to adaptively represent the grasping attribute of objects, for resolving angle conflicts in training and simplifying pixel-level labeling. Lastly, the adaptive feature fusion and grasp-aware network (AFFGA-Net) is proposed to predict pixel-level OAR-models on RGB images. AFFGA-Net improves the robustness in unstructured scenarios by using hybrid atrous spatial pyramid and adaptive decoder connected in sequence. On the public Cornell dataset and actual objects, our structure achieves 99.09% and 98.0% grasp detection accuracy, respectively. In over 2400 robotic grasp trials, our structure achieves an average success rate of 98.77% in single-object scenarios and 93.69% in cluttered scenarios. Moreover, AFFGA-Net completes a grasp detection pipeline within 15 ms. [ABSTRACT FROM AUTHOR]

Driver attention prediction can solve the problem of ‘Where should the driver pay attention?’, Most previous methods are designed to predict regional attention with redundant regions. Furthermore, popular spatial-temporal feature extraction networks such as ConvLSTM and 3D-CNN are difficult to achieve real-time. To overcome these difficulties, we propose an Adaptive Short-temporal Induced Aware Fusion Network (ASIAF-Net) for region-level and object-level driver attention prediction. In ASIAF-Net, we design an Attention Related Spatial Feature Encoder (AF-Encoder) and an Induced Aware Fusion Network (IAF-Net) as the main network; with an Association Analysis Cell (AAC), the AF-Encoder makes it possible to effectively capture the relationship information of different objects. Considering most vital visual cues from moving objects, we propose a Self-adaptive Short-temporal Feature Extraction Module (SSFE-Module) to obtain inter-frame motion features. In IAF-Net, a Multi-scale Driver Attention Region Prediction Branch is designed to predict the regional attention, and an Object Saliency Estimation Branch is proposed to fuse the perception results and the regional attention map to estimate the object-level attention. Experiments show that the proposed ASIAF-Net can predict driver’s attention on regions and objects more robustly and precisely than state-of-the-art methods on three datasets, and that it achieves real-time on our ADAS platform. [ABSTRACT FROM AUTHOR]

Machine vision based vehicle re-identification (ReID) plays an important role in some Intelligent Transportation Systems (ITS). Yet, most previous methods mainly focus on fixed surveillance cameras instead of Unmanned Aerial Vehicle (UAV). With high flexibility, the UAV-based vehicle ReID problem has some special challenges including complicated shooting angles, low discrimination of top-down features, and large variance in vehicle scales, etc. To overcome these challenges, we propose a novel structure for UAV-based vehicle ReID without license plates. Firstly, a triple-head segmentation net is proposed for segmenting UAV-captured vehicles under different heights and directions. Secondly, a posture calibration model is designed to uniform the vehicle postures based on the segmentation results, with the purpose of reducing the influence of different postures. Thirdly, the novel Cross-View & Hard-Sensitive Metric Learning (CHSML) method is proposed to train a ReID network with cross-view training constraint and hard sensitive principles; the mechanism of CHSML takes the cross-view samples of same ID as a training unit to learn the potential visual relationship in cross-view and builds a hard sensitive weight matrix to make learning more focus on hard samples, which improves the low ReID accuracy brought by cross-view or hard samples. Moreover, to facilitate the research of UAV-based vehicle ReID, a large-scale UAV-based vehicle ReID dataset called VeRi-UAV is released with 17 516 vehicles of 453 IDs. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. [ABSTRACT FROM AUTHOR]

On-board vision based traffic risk assessment is a challenging task for intelligent driving systems, which has some special challenges including spatial-temporal feature extraction, different judgements of risks, real-time requirement, lacking data, etc. To overcome these difficulties, we propose a novel Temporal Shift and Spatial Attention based Two-stream Network (TSSAT-Net) for on-board vision based traffic risk assessment. Firstly, we build new judgement measures that integrate actual driving experience and scenario complexity, and release an on-board vision based traffic risk assessment dataset. Secondly, a novel weighted Temporal Shift Module (weighted-TSM) based two-stream network is proposed; unlike previous methods that rely on complex and time-consuming 3D CNN or LSTM calculation, the proposed two-stream network can effectively extract spatial-temporal features using a weighted temporal shift mechanism with just 2D CNN calculation requirement. Thirdly, a spatial and channel attention mechanism is proposed to make the TSSAT-Net more focus on the features closely related to traffic risks, avoiding redundant information in complex traffic scenarios. Experiments based on the released comprehensive dataset show that our method achieves the state-of-the-art classification accuracy in real-time. [ABSTRACT FROM AUTHOR]

Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous line of interest (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel spatio-temporal counting feature (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the counting Long Short-Term Memory (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time. [ABSTRACT FROM AUTHOR]

Driver gaze zone estimation is an important task in Advanced Driver Assistance Systems (ADAS), which suffers difficulties including head pose, capture direction, glass occlusion, and real-time requirement, etc. Most previous methods combine face modalities and head pose using concat process, which may result in over-fitting due to the unbalanced dimension. Focusing on gaze zone estimation problems, we propose the Head Pose Fusion Assisted supervision & Eye Region Weighted Encoding (HP-ERW) structure, which fuses head pose attribute and face modalities together through spatial attention and Kronecker product mechanisms. Firstly, we introduce a pre-processing module dealing with head pose and face information, with the purpose of extracting input vectors and improving the fusion speed of the HP-ERW structure. Secondly, an Eye Region Weighted Encoding Network (ERW-Net) based on spatial attention is proposed to strengthen the networks perception ability for encoding features. Finally, we propose a dual-channel Head Pose Fusion Network (HP-Net) based on the Kronecker product mechanism, with the purpose of fusing head pose and improving the estimation accuracy. Experiments show that the HP-ERW outperforms compared existing methods on several public datasets. The designed ADAS using the proposed method achieves 23.5 fps real-time application with small memory requirement of 4,884 KB. [ABSTRACT FROM AUTHOR]

It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust. [ABSTRACT FROM AUTHOR]

Though license plate detection has been successfully applied in some commercial products, the detection of small and vague license plates in real applications is still an open problem. In this paper, we propose a novel hybrid cascade structure for fast detecting small and vague license plates in large and complex visual surveillance scenes. For rapid license plate candidate extraction, we propose two cascade detectors, including the Cascaded Color Space Transformation of Pixel detector and the Cascaded Contrast-Color Haar-like detector; these two cascade detectors can do coarse-to-fine detection in the front and in the middle of the hybrid cascade. In the end of the hybrid cascade, we propose a cascaded convolutional network structure (Cascaded ConvNet), including two detection-ConvNets and a calibration-ConvNet, which is designed to do fine detection. Through experiments with different evaluation data sets with many small and vague plates, we show that the proposed framework is able to rapidly detect license plates with different resolutions and different sizes in large and complex visual surveillance scenes. [ABSTRACT FROM AUTHOR]

This paper aims to develop a simple but effective method that can estimate the number of people in still images. Inspired by the successful applications of deep learning and the appearance of crowd, we design a count-net based on Convolutional Neural Network (CNN). The count-net takes the appearance of crowd as auxiliary mechanism, thus filtering out most of the backgrounds and focusing more on people’s heads. In addition, we adopt a separated-aggregated framework since the structure of crowd in an image is rarely uniform. Firstly, we separate the crowd image into patches to treat different spatial locations discriminatively. Afterwards, these patches and the manual marked labels are fed into a count-net to train the parameters of this network. For practical application, only the counting channel of the count-net is demanded. Finally, the numbers of people in patches that belong to one image are summed as the output of the whole framework. Experimental results conducted on UCF and AHU-CROWD datasets demonstrate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

To the question of feature selection and multi-feature fusion in facial expression recognition, a novel fusion model is proposed in this paper based on evidence theory and local feature operator. First, the facial image is divided into several regions with significant recognition features, and the Local Binary Pattern (LBP) textural features of the regions are extracted. Then, the LBP histograms in the local regions are connected into a single histogram list, and Chi-square distance is used as the similarity measure to establish the guidelines for evidence synthesis. Finally, the Dempster-Shafer evidence inference theory (D-S evidence theory) is adopted to accomplish the feature vector fusion of all components and the class judgment of facial expression is performed. Experiment shows that the method is simple and effective, which has a high recognition rate and can improve the performance of the facial expression recognition system to some extent. [ABSTRACT FROM AUTHOR]

Background: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. Methods: A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. Results: The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. Conclusion: The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications. [ABSTRACT FROM AUTHOR]

The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.5.051202] [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-performance traffic sign recognition (TSR) framework to rapidly detect and recognize multiclass traffic signs in high-resolution images. This framework includes three parts: a novel region-of-interest (ROI) extraction method called the high-contrast region extraction (HCRE), the split-flow cascade tree detector (SFC-tree detector), and a rapid occlusion-robust traffic sign classification method based on the extended sparse representation classification (ESRC). Unlike the color-thresholding or extreme region extraction methods used by previous ROI methods, the ROI extraction method of the HCRE is designed to extract ROI with high local contrast, which can keep a good balance of the detection rate and the extraction rate. The SFC-tree detector can detect a large number of different types of traffic signs in high-resolution images quickly. The traffic sign classification method based on the ESRC is designed to classify traffic signs with partial occlusion. Instead of solving the sparse representation problem using an overcomplete dictionary, the classification method based on the ESRC utilizes a content dictionary and an occlusion dictionary to sparsely represent traffic signs, which can largely reduce the dictionary size in the occlusion-robust dictionaries and achieve high accuracy. The experiments demonstrate the advantage of the proposed approach, and our TSR framework can rapidly detect and recognize multiclass traffic signs with high accuracy. [ABSTRACT FROM PUBLISHER]

This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common features of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information. [ABSTRACT FROM PUBLISHER]

License plate recognition (LPR) is one of the most important elements affecting intelligent transportation systems. A number of LPR techniques have been proposed. Humans are good target recognition systems. In other words, humans easily recognize common objects. In this paper, the researchers present a novel method of recognizing Chinese license plates. The method is based on the Human Vision Attention Mechanism (HVAM) and uses Chinese license plates as the targets. The research consists of three stages. The first stage involved finding and identifying license plates in videos of moving vehicles. The second stage separated each license plate into the seven characters. In the third stage, the character recognizer extracted some salient features of Chinese characters and used a multi-stage classifier to recognize each character on the license plate. In the experiment locating license plates, 1176 images taken from various scenes and conditions were employed. The method failed to identify the license plates in only 27 of the images; resulting in a license plate location rate of success of 97.7%. In the experiment for identifying license characters, 1149 images were used, from which license plates had been successfully located. The method failed to identify the characters in 45 of these images giving a success rate of 96.1%. Combining the above two rates, the overall rate of success for our LPR is 93.9%. [ABSTRACT FROM AUTHOR]

With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works. [ABSTRACT FROM AUTHOR]

You Only Look Once (YOLO) deep network can detect objects quickly with high precision and has been successfully applied in many detection problems. The main shortcoming of YOLO network is that YOLO network usually cannot achieve high precision when dealing with small-size object detection in high resolution images. To overcome this problem, we propose an effective region proposal extraction method for YOLO network to constitute an entire detection structure named ACF-PR-YOLO, and take the cyclist detection problem to show our methods. Instead of directly using the generated region proposals for classification or regression like most region proposal methods do, we generate large-size potential regions containing objects for the following deep network. The proposed ACF-PR-YOLO structure includes three main parts. Firstly, a region proposal extraction method based on aggregated channel feature (ACF) is proposed, called ACF based region proposal (ACF-PR) method. In ACF-PR, ACF is firstly utilized to fast extract candidates and then a bounding boxes merging and extending method is designed to merge the bounding boxes into correct region proposals for the following YOLO net. Secondly, we design suitable YOLO net for fine detection in the region proposals generated by ACF-PR. Lastly, we design a post-processing step, in which the results of YOLO net are mapped into the original image outputting the detection and localization results. Experiments performed on the Tsinghua-Daimler Cyclist Benchmark with high resolution images and complex scenes show that the proposed method outperforms the other tested representative detection methods in average precision, and that it outperforms YOLOv3 by 13.69 % average precision and outperforms SSD by 25.27 % average precision. [ABSTRACT FROM AUTHOR]

Rectangular features have been widely used in different cascade‐based object detection areas. Previous rectangular features contain no relationship of different colour channels, which makes the previous rectangular features weak in expressing objects with colour contrast. To overcome this shortcoming, a series of rectangular features called split‐level colour Haar‐like (SC‐Haar‐like) features are proposed. There are different colour channels in the SC‐Haar‐like features, and the different colour channels have close relationship with each other based on the split‐level structure. The experiments on face detection show that the SC‐Haar‐like features outperform previous rectangular features in terms of detection rate and false alarm rate. [ABSTRACT FROM AUTHOR]

With rapid calculation speed and relatively high accuracy, the AdaBoost-based detection framework has been successfully applied in some real applications of machine vision-based intelligent systems. The main shortcoming of the AdaBoost-based detection framework is that the off-line trained detector cannot be transfer retrained to adapt to unknown application scenes. In this paper, a new transfer learning structure based on two novel methods of supplemental boosting and cascaded ConvNet is proposed to address this shortcoming. The supplemental boosting method is proposed to supplementally retrain an AdaBoost-based detector for the purpose of transferring a detector to adapt to unknown application scenes. The cascaded ConvNet is designed and attached to the end of the AdaBoost-based detector for improving the detection rate and collecting supplemental training samples. With the added supplemental training samples provided by the cascaded ConvNet, the AdaBoost-based detector can be retrained with the supplemental boosting method. The detector combined with the retrained boosted detector and cascaded ConvNet detector can achieve high accuracy and a short detection time. As a representative object detection problem in intelligent transportation systems, the traffic sign detection problem is chosen to show our method. Through experiments with the public datasets from different countries, we show that the proposed framework can quickly detect objects in unknown application scenes. [ABSTRACT FROM AUTHOR]

Early action prediction aiming to recognize which classes the actions belong to before they are fully conveyed is a very challenging task, owing to the insufficient discrimination information caused by the domain gaps among different temporally observed domains. Most of the existing approaches focus on using fully observed temporal domains to "guide" the partially observed domains while ignoring the discrepancies between the harder low-observed temporal domains and the easier highly observed temporal domains. The recognition models tend to learn the easier samples from the highly observed temporal domains and may lead to significant performance drops on low-observed temporal domains. Therefore, in this article, we propose a novel temporally observed domain contrastive network, namely, TODO-Net, to explicitly mine the discrimination information from the hard actions samples from the low-observed temporal domains by mitigating the domain gaps among various temporally observed domains for 3-D early action prediction. More specifically, the proposed TODO-Net is able to mine the relationship between the low-observed sequences and all the highly observed sequences belonging to the same action category to boost the recognition performance of the hard samples with fewer observed frames. We also introduce a temporal domain conditioned supervised contrastive (TD-conditioned SupCon) learning scheme to empower our TODO-Net with the ability to minimize the gaps between the temporal domains within the same action categories, meanwhile pushing apart the temporal domains belonging to different action classes. We conduct extensive experiments on two public 3-D skeleton-based activity datasets, and the results show the efficacy of the proposed TODO-Net.

Early activity prediction/recognition aims to recognize action categories before they are fully conveyed. Compared to full-length action sequences, partial video sequences only provide insufficient discrimination information, which makes predicting the class labels for some similar activities challenging, especially when only very few frames can be observed. To address this challenge, in this paper, we propose a novel meta negative network, namely, Magi-Net, that utilizes a contrastive learning scheme to alleviate the insufficiency of discriminative information. In our Magi-Net model, the positive samples are generated by augmenting an input anchor conditioned on all observation ratios, while the negative samples are selected from a trainable negative look-up memory (LUM) table, which stores the training samples and the corresponding misleading categories. Furthermore, a meta negative sample optimization strategy (MetaSOS) is proposed to boost the training of Magi-Net by encouraging the model to learn from the most informative negative samples via a meta learning scheme. Extensive experiments are conducted on several public skeleton-based activity datasets, and the results show the efficacy of the proposed Magi-Net model.

Surgical instrument detection in robot-assisted surgery videos is an import vision component for these systems. Most of the current deep learning methods focus on single-tool detection and suffer from low detection speed. To address this, the authors propose a novel frame-by-frame detection method using a cascading convolutional neural network (CNN) which consists of two different CNNs for real-time multi-tool detection. An hourglass network and a modified visual geometry group (VGG) network are applied to jointly predict the localisation. The former CNN outputs detection heatmaps representing the location of tool tip areas, and the latter performs bounding-box regression for tool tip areas on these heatmaps stacked with input RGB image frames. The authors' method is tested on the publicly available EndoVis Challenge dataset and the ATLAS Dione dataset. The experimental results show that their method achieves better performance than mainstream detection methods in terms of detection accuracy and speed.

The nonsubsampled contourlet transform (NSCT) has properties of multiresolution, localization, directionality, and anisotropy. The directionality property permits it to resolve intrinsic directional features that characterize the analyzed image. In this paper, we present a bottom-up salient object detection approach fusing global and local information based on NSCT. Images are first decomposed by applying NSCT. The coefficients of bandpass subbands are categorized and optimized accordingly to get better representation. Then feature maps are obtained by performing the inverse NSCT on these optimized coefficients. The global and local saliency maps are generated from these feature maps. Global saliency is obtained by utilizing the likelihood of features, and local saliency is measured by calculating the local self-information. In the end, the final saliency map is computed by fusing the global and local saliency maps together. Experimental results on MSRA 10K demonstrate the effectiveness and promising performance of our proposed method.