Real-time Multiple Target Tracking Research Articles

Continuous localization and mapping of a pan–tilt–zoom camera for wide area tracking

Pan---tilt---zoom (PTZ) cameras are well suited for object identification and recognition in far-field scenes. However, the effective use of PTZ cameras is complicated by the fact that a continuous online camera calibration is needed and the absolute pan, tilt and zoom values provided by the camera actuators cannot be used because they are not synchronized with the video stream. So, accurate calibration must be directly extracted from the visual content of the frames. Moreover, the large and abrupt scale changes, the scene background changes due to the camera operation and the need of camera motion compensation make target tracking with these cameras extremely challenging. In this paper, we present a solution that provides continuous online calibration of PTZ cameras which is robust to rapid camera motion, changes of the environment due to varying illumination or moving objects. The approach also scales beyond thousands of scene landmarks extracted with the SURF keypoint detector. The method directly derives the relationship between the position of a target in the ground plane and the corresponding scale and position in the image and allows real-time tracking of multiple targets with high and stable degree of accuracy even at far distances and any zoom level.

Multiple Target Tracking with RF Sensor Networks

RF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We apply radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We introduce methods, inspired by machine vision and adapted to the peculiarities of RTI, that enable accurate and real-time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to four targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets.

Errata: Feedback strategy on real-time multiple target tracking in cognitive vision system

Errata: Feedback strategy on real-time multiple target tracking in cognitive vision system

Feedback strategy on real-time multiple target tracking in cognitive vision system

Under pedestrian and vehicle mixed traffic conditions, the potential accident rate is high due to a complex traffic environment. In order to solve this problem, we present a real-time cognitive vision system. In the scene-capture level, foreground objects are extracted based on the combination of spatial and temporal information. Then, a coarse-to-fine algorithm is employed in tracking. After filtering-based normal tracking, problems of the target blob missing, merging, and splitting are resolved by the adaptive tracking modification method in fine tracking. For greater robustness, the key idea of our approach is adaptively adjusting the classification sensibility of each pixel by employing tracking results as feedback cues for target detection in the next frame. On the basis of the target trajectories, behavior models are evaluated according to a decision logic table in the behavior-evaluation level. The decision logic table is set based on rules of real scenes. The resulting system interprets different kinds of traffic behavior and warns in advance. Experiments show robust and accurate results of abnormality detection and forewarning under different conditions. All the experimental results run at real-time frame rates (⩾25 fps) on standard hardware. Therefore, the system is suitable for actual Intelligent Traffic System applications.