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Raw Trajectory Rectification via Scene-Free Splitting and Stitching

Journal of Computer Science & Technology 2015年第2期30卷 364-372页

作者：郭春超胡晓军赖剑煌石世昌陈世哲 School of Information Science and Technology Sun Yat-sen University Guangzhou 510006 China

Trajectories carry rich motion cues and thus have been leveraged to many high-level computer vision tasks. Due to the easy implementation of simple trackers, most previous work on trajectory-based applications utilizes raw tracking outputs without explicitly considering tracking errors. Reliable trajectories are prerequisite for modeling and recognizing high-level behaviors. Therefore, this paper tackles such problems by rectifying raw trajectories, which aims to post-process existing trajectories. Our approach firstly splits them into short tracks, and then infers identity ambiguity to remove unqualified detection responses. At last, short tracks are stitched via maximum bipartite graph matching. This post- processing is completely scene-free. Results of trajectory rectification and their benefits are both evaluated on two challenging datasets. Results demonstrate that rectified trajectories are conducive to high-level tasks and the proposed approach is also competitive with state-of-the-art multi-target tracking methods.

关键词： raw trajectory rectification trajectory post-processing identity ambiguity multi-target tracking activity classification

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Adaptive Weighted Flow Net Algorithm for Human activity Recognition Using Depth Learned Features

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Computer Systems Science & Engineering 2023年第8期46卷 1447-1469页

作者： G.Augusta Kani P.Geetha Department of Information Science and Technology Anna UniversityChennai600025TamilnaduIndia

Human activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior *** many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral ***,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this *** most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among ***,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi ***,we identify the key and essential frames of each activity using histogram ***,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in ***,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity ***,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance *** work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification *** proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as *** these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.

关键词： activity classification discrete wavelet object detection AWFN

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