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Smart city article details
| Title | A Multi-Hypothesis Tracker With Enhanced Appearance Model For Generic Crowded Scene |
|---|---|
| ID_Doc | 2799 |
| Authors | Wang C.; Ke W.; Wu Z.; Xiong Z. |
| Year | 2022 |
| Published | 6th IEEE International Conference on Universal Village, UV 2022 |
| DOI | http://dx.doi.org/10.1109/UV56588.2022.10185491 |
| Abstract | Pedestrian tracking studies have been facilitated by a large amount of surveillance apparatus in the city while also raising public privacy concerns. In this paper, we propose X-Tracking, a privacy-aware pedestrian tracking paradigm designed for vision systems in Smart City. It allows low-cost compatibility with existing surveillance architecture. To protect entities' privacy, X-Tracking uses video pre-processing with desensitization so that identity information is unexposed to the tracking algorithm. We implement system-level privacy protection by redesigning the tracking framework that decouples all services based on a single responsibility principle. Then, we elaborate on the roles, behaviors, and protocols used in the new system and illustrate how the paradigm strikes a favorable balance between privacy protection and convenience services. Furthermore, we propose a new tracking task that aims to track humans in masking surveillance video. It is comparable to previous tracking tasks but considering the target with a distorted appearance poses new challenges for visual tracking. Finally, we evaluate the baseline algorithm on the task with a demo dataset. © 2022 IEEE. |
| Author Keywords | generic object tracking; multi-hypothesis tracking; multi-object tracking; visual tracking |
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