Search

Computer vision techniques can be used to detect site objects for identifying noncompliances that could lead to safety incidents. However, existing methods are limited in covering diverse hazard scenarios, detecting site objects with imbalanced distributions, and recognizing their intricate attributes to describe their conditions and functionality. To address these gaps, this paper proposes a deep learning-based method for identifying multiple fall-related objects and their associated attributes. The proposed method consists of three submethods: (1) a method for developing relevant datasets by retrieving images from open resources; (2) a method for few-shot object detection, which deals with imbalanced distributions; and (3) a method for attribute recognition to add semantic descriptions to the detected objects. The proposed method achieved an average precision and recall of 88.2% and 79.5% for few-shot object detection and 94.8% and 95.7% for attribute recognition, respectively, which indicates good performance. • Deep learning-based method to detect information for field compliance checking. • Conducts few-shot object detection for objects with imbalanced distribution. • Conducts attribute recognition to add descriptive condition information to objects. • Achieved good performance in extracting site visual information. [ABSTRACT FROM AUTHOR]

The human brain has been a main source of inspiration for designing deep learning models. Recently, inspired by the specialized functions of two cerebral hemispheres in processing low and high spatial frequency information, some dual-path neural networks with global and local branches have been proposed to deal with both coarse- and fine-grained visual tasks simultaneously. However, in existing works, the interhemispheric communication mechanism, which is responded by the corpus callosum, the largest white matter structure in the human brain that connecting the left and right cerebral hemispheres, is still not fully explored and exploited. This paper aims to explore how the corpus callosum can inspire us to enable transfer and integration of information between global and local branches in hemisphere-inspired artificial neural networks, such that one branch can leverage the other's learned knowledge and benefit each other. To this end, we propose a gated intercommunication unit to selectively transfer useful knowledge between the two branches via attention mechanisms to alleviate the negative transfer. Experiments on sb-MNIST and two pedestrian attribute datasets show that the proposed method outperforms the compared ones in most cases.

Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person's soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specific challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description. [ABSTRACT FROM AUTHOR]

Person attribute recognition, i.e., the prediction of a fixed set of semantic attributes given an image of a person, becomes an important topic in the field of computer vision. Recently, methods based on convolutional neural networks have shown outstanding performance in this area. They usually employ a CNN network to mine the shared feature representation followed by several layers for attribute classification. To improve the representation ability of the model, many methods element-add or concatenate coarse and fine feature maps to fuse information at different feature levels. However, these methods didn't fully exploit the interaction of multi-level convolutional feature maps for person attribute analysis and not consider the correlation of attributes for the same person. In this paper, we introduce a kind of correlation feature, which exploits the high order interaction of coarse and fine feature maps to capture the robust feature representation from multi-level convolution layers as the image representation for person attribute recognition. Moreover, we propose an intraperson attribute loss to explicitly model the correlation of attributes for the same person. We experiment our proposed model on CIFAR-10 dataset, Berkeley Human Attributes dataset, PA-100 K dataset, and experimental results show the better performance of the feature representation and the effectiveness of intra-person attribute loss. [ABSTRACT FROM AUTHOR]

In recent years, visual attributes, which are mid-level representations that describe human-understandable aspects of objects and scenes, have become a popular topic of computer vision research. Visual attributes are being used in various tasks, including object recognition, people search, scene recognition, and many more. A critical step in attribute recognition is the extraction of low-level features, which encodes the local visual characteristics in images, and provides the representation used in the attribute prediction step. In this work, we explore the effects of utilizing different low-level features on learning visual attributes. In particular, we analyze the performance of various shape, color, texture and deep neural network features. Experiments have been carried out on four different datasets, which have been collected for different visual recognition tasks and extensive evaluations have been reported. Our results show that, while the supervised deep features are effective, using them in combination with low-level features can lead to significant improvements in attribute recognition performance. [ABSTRACT FROM AUTHOR]

Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application. [ABSTRACT FROM AUTHOR]