Junzhi Guan, Peter Van Hese, Jorge Oswaldo Niño-Castañeda, Nyan Bo Bo, Sebastian Gruenwedel, Dirk Van Haerenborgh, Dimitri Van Cauwelaert, Peter Veelaert, Wilfried Philips
In this paper, we proposes a people tracking system composed of multiple calibrated smart cameras and one fusion server which fuses the information from all cameras. Each smart camera estimates the ground plane positions of people based on the current frame and feedback from the server from the previous time. Correlation coefficient based template matching, which is invariant to illumination changes, is proposed to estimate the position of people in each smart camera. Only the estimated position and the corresponding correlation coefficient are sent to the server. This minimal amount of information exchange makes the system highly scalable with the number of cameras. The paper focuses on creating and updating a good template for the tracked person using feedback from the server. Additionally, a static background image of the empty room is used to improve the results of template matching. We evaluated the performance of the tracker in scenarios where persons are often occluded by other persons or furniture, and illumination changes occur frequently e.g., due to switching the light on or off. For two sequences (one minute for each, one with table in the room, one without table) with frequent illumination changes, the proposed tracker never lose track of the persons. We compare the performance of our tracking system to a state-of-the-art tracking system. Our approach outperforms it in terms of tracking accuracy and people loss.
Nyan Bo Bo, Peter Van Hese, Junzhi Guan, Sebastian Gruenwedel, Jorge Niño-Castañeda, Dimitri Van Cauwelaert, Dirk Van Haerenborgh, Peter Veelaert, Wilfried Philips
Many computer vision based applications require reliable tracking of multiple people under unpredictable lighting conditions. Many existing trackers do not handle illumination changes well, especially sudden changes in illumination. This paper presents a system to track multiple people reliably even under rapid illumination changes using a network of calibrated smart cameras with overlapping views. Each smart camera extracts foreground features by detecting texture changes between the current image and a static background image. The foreground features belonging to each person are tracked locally on each camera but these local estimates are sent to a fusion center which combines them to generate more accurate estimates. The nal estimates are fed back to all smart cameras, which use them as prior information for tracking in the next frame. The texture based approach makes our method very robust to illumination changes. We tested the performance of our system on six video sequences, some containing sudden illumination changes and up to four walking persons. The results show that our tracker can track multiple people accurately with an average tracking error as low as 8 cm even when the illumination varies rapidly. Performance comparison to a state-of-the-art tracking system shows that our method outperforms.
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