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- Kristanl, Matej, et al.
(author)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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In: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 2. |
- Liao, Yuan, 1991, et al.
(author)
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Detection of driver cognitive distraction: An SVM based real-time algorithm and its comparison study in typical driving scenarios
- 2016
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In: IEEE Intelligent Vehicles Symposium, Proceedings. 2016 IEEE Intelligent Vehicles Symposium, IV 2016; Gotenburg; Sweden; 19-22 June 2016. - 9781509018215 ; 2016-August:Art no 7535416, s. 394-399
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Conference paper (peer-reviewed)abstract
- Detection of driver cognitive distraction is critical for active safety systems of road vehicles. Compared with visual distraction, cognitive distraction is more challenging for detection due to the lack of apparent exterior features. This paper presents a novel real-time detection algorithm for driver cognitive distraction by using support vector machine (SVM). Data are collected from 26 subjects, driving in typical urban and highway scenarios in a simulator. The chosen urban scenario is the stop-controlled intersection and the highway scenario is the speed-limited highway. Driver cognitive distraction while driving is induced by clock tasks which compete with the main driving tasks for visuospatial short working memory. For each subject, distracted driving instances and the equal number of non-distracted driving instances were collected (24 for urban scenario and 20 for highway scenario in total). Features concerning both driving performance and eye movement are used for training and validation. The proposed algorithm have correct rate of 93.0% and 98.5% for highway and urban scenarios respectively. Results also show that driver distraction can be recognized 6.5 s to 9.0 s after its happening, indicating good performance of the detection algorithm.
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