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      Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

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      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Interactive learning, eye-tracker, target tracking, TLD

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          It is difficult for a human operator to monitor multiple video streams in real-time surveillance tasks. A variety of machine learning algorithms have been proposed for target tracking, but human intervention is still necessary because the algorithms have limitations to detect and recover from tracking failures. In this paper, we propose a supervisory target tracking method for multi-video surveillance tasks based on hTLD (human-in-the-loop Tracking-Learning-Detection). The human operator can monitor both the original video streams and the calculated confidence values of the tracking results. Human intervention allows to update the training data for TLD whenever a tracking failure is observed, by manually drawing a bounding box or using an eye-tracker to relocate the target. Meanwhile, an interactive on-line learning algorithm is used to learn the relations between the confidence values and the tracking results. In this way, the confidence values help avoid unnecessary intervention caused by false alarms of tracking failures. Experimental results have demonstrated that the proposed method can reduce the task completion time.

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          This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of "experts": (1) P-expert estimates missed detections, and (2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.
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            Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification

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              An iterative image registration technique with an application to stereo vision


                Author and article information

                July 2018
                July 2018
                : 1-5
                College of Intelligent Systems, National University of Defense Technology

                De Ya Lu 109, Changsha, Hu’nan, China, 410073
                © Wang et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page):
                Electronic Workshops in Computing


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