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The paper addresses the problem of rain streak removal from videos. While, Rain streak removal from scene is important but a lot of research in this area, robust and real time algorithms is unavailable in the market. Difficulties in the rain streak removal algorithm arises due to less visibility, less illumination, and availability of moving camera and objects. The challenge that plagues rain streak recovery algorithm is detecting rain streaks and replacing them with original values to recover the scene. In this paper, we discuss the use of photometric and chromatic properties for rain detection. Updated Gaussian Mixture Model (Updated GMM) has detected moving objects. This rain streak removal algorithm is used to detect rain streaks from videos and replace it with estimated values, which is equivalent to original value. The spatial and temporal properties are used to replace rain streaks with its original values.

Object detecting and tracking is an important technique used in diverse applications of machine vision, and has made great progress with the prevalence of artificial intelligence technology, among which the detecting and tracking moving object under dynamic scenes is more challenging for high requirements on real-time performance and reliability. Essentially analyzing, object detecting and tracking need to classify the objects and background into two different categories according to different features, where the detecting and tracking drift caused by noisy background can be effectively handled by robust maximum margin classifier, such as one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to be high, which limits its wide applications to various fields. Inspired by the idea proposed by Support Vector Data Description (SVDD), in this paper we present a novel SVDD-based algorithm to efficiently deal with detecting and tracking moving object under dynamic scenes. The experimental results on synthetic, benchmark data and real-world videos demonstrate the competitive performances of the proposed method

Stereo matching is one of the most important and challenging subjects in the field of stereo vision. The disparity obtained in stereo matching can represent depth information in 3-D world to a great extent and shows great importance in stereo field. In general, stereo-matching methods primarily emphasize static image. However, the information provided by dynamic scene can be used fully and effectively to improve the results of stereo matching for dynamic scene, such as video sequences. In this paper, we propose a dynamic scene-based local stereo-matching algorithm which integrates a cost filter with motion flow of dynamic video sequences. In contrast to the existing local approaches, our algorithm puts forward a new computing model which fully considers motion information in dynamic video sequences and adds motion flow to calculate suitable support weight for accurately estimating disparity. Our algorithm can perform as an edge-preserving smoothing operator and shows improved behavior near the moving edges. The experimental results show that the proposed method achieves a better depth map and outperforms other local stereo-matching methods in disparity evaluation.

Recent research progress on the topic of human visual attention allocation in scene perception and its simulation is based mainly on studies with static images. However, natural vision requires us to extract visual information that constantly changes due to egocentric movements or dynamics of the world. It is unclear to what extent spatio-temporal regularity, an inherent regularity in dynamic vision, affects human gaze distribution and saliency computation in visual attention models. In this free-viewing eye-tracking study we manipulated the spatio-temporal regularity of traffic videos by presenting them in normal video sequence, reversed video sequence, normal frame sequence, and randomised frame sequence. The recorded human gaze allocation was then used as the ‘ground truth’ to examine the predictive ability of a number of state-of-the-art visual attention models. The analysis revealed high inter-observer agreement across individual human observers, but all the tested attention models performed significantly worse than humans. The inferior predictability of the models was evident from indistinguishable gaze prediction irrespective of stimuli presentation sequence, and weak central fixation bias. Our findings suggest that a realistic visual attention model for the processing of dynamic scenes should incorporate human visual sensitivity with spatio-temporal regularity and central fixation bias.

This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach. Dynamic textures are commonplace in natural scenes, and exhibit complex patterns of appearance and motion (e.g. water, smoke, swaying foliage). These are difficult for existing segmentation algorithms, often violate the brightness constancy assumption needed for optical flow, and have complex segment characteristics beyond uniform appearance or motion. Our solution uses custom spatiotemporal filters that capture texture and motion cues, along with a novel metric-learning framework that optimizes this representation for specific objects and scenes. This is used within a hierarchical, graph-based segmentation setting, yielding state-of-the-art results for dynamic texture segmentation. We also demonstrate the applicability of our approach to general object and motion segmentation, showing significant improvements over unsupervised segmentation and results comparable to the best task specific approaches.

Acquisition of dynamic dense 3D shape data is of increasing importance in computer vision with applications in various disciplines. In this paper, we investigate the performance of a unique high-speed range sensor based on the stereo vision principle for 3D shape acquisition of animals. The investigation reveals some characteristics of the current version of the sensor with respect to its physical parameters, which suggest an more appropriate configuration of the sensor in real data acquisition scenarios. Due to the novelty of the sensor and the application, we believe that our evaluation of the sensor's performance will inspire new applications to follow using the dynamic 3D acquisition technology of similar types.

Recently, generation of novel views from images acquired by multiple cameras has been investigated. It can be applied to telepresence effectively. Most conventional methods need some assumptions about the scene such as a static scene and limited positions of objects. In this paper, we propose a new method for generating novel view images of a dynamic scene with a wide view, which does not depend on the scene. The images acquired from omni-directional cameras are first divided into static regions and dynamic regions. The novel view images are then generated by applying a morphing technique to static regions and by computing visual hulls for dynamic regions in real-time. In experiments, we show that a prototype system can generate novel view images in real-time from live video streams., ICIP 2005 : 12th IEEE International Conference on Image Processing , Sep 11-14, 2005 , Genova, Italy