Open AccessJournal Article
Tracking-based moving object detection
5
TL;DR: The results show that the proposed approach can reliably detect moving objects even in challenging situations, and can process videos in real time, without the effect of time delay.
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Abstract: We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.
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Citations
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Moving Object Detection and Segmentation for Remote Aerial Video Surveillance
Michael Teutsch
- 09 Oct 2020
TL;DR: Novel robust and fast object detection and segmentation approaches improve the baseline TBD and outperform current state-of-the-art methods.
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Robust object tracking via adaptive sparse representation
Rahman Khorsandi,Mohamed Abdel-Mottaleb +1 more
- 01 Dec 2015
TL;DR: A robust object tracking system capable of handling pose and scale variations and using K-SVD method for dictionary learning in order to decrease the number of atoms and improve the processing speed.
A dynamic online background modeling framework for moving object detection from airborne videos
Xiaosong Lan,Shuxiao Li,Chengfei Zhu,Feimo Li,Hongxing Chang +4 more
- 01 Dec 2015
TL;DR: A dynamic online background modeling framework to facilitate the adaption of the available background subtraction algorithms for moving object detection from airborne videos that can avoid accumulated stabilization errors and handle the pixels near the frame boundary well.
2
Sparse representation and dictionary learning for biometrics and object tracking
Rahman Khorsandi
- 01 Jan 2015
TL;DR: A fully automated system for recognition from ear images based upon sparse representation, gender classification and object tracking, and a robust tracking system based on adaptive sparse representation and feedback are presented.
Robust Stabilised Visual Tracker for Vehicle Tracking
TL;DR: A novel algorithm is developed which simultaneously takes care of video stabilisation and target tracking, and accurate tracking results have been obtained in destabilised videos.
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TL;DR: A novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection, and develops a novel learning method (P-N learning) which estimates the errors by a pair of “experts”: P-expert estimates missed detections, and N-ex Expert estimates false alarms.
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Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Life Spans
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