Abstract
This paper deals with the problem of multi-target tracking in a distributed camera network using the square-root cubature information filter (SCIF). SCIF is an efficient and robust nonlinear filter for multi-sensor data fusion. In camera networks, multiple cameras are arranged in a dispersed manner to cover a large area, and the target may appear in the blind area due to the limited field of view (FOV). Besides, each camera might receive noisy measurements. To overcome these problems, this paper proposes a novel multi-target square-root cubature information weighted consensus filter (MTSCF), which reduces the effect of clutter or spurious measurements using joint probabilistic data association (JPDA) and proper weights on the information matrix and information vector. The simulation results show that the proposed algorithm can efficiently track multiple targets in camera networks and is obviously better in terms of accuracy and stability than conventional multi-target tracking algorithms.
Highlights
With the rapid development of image processing, sensor and semiconductor technology, the availability of inexpensive hardware, such as CMOS cameras, that are able to ubiquitously capture video content from the environment has fostered the development of camera networks [1]
This paper proposes a novel consensus filter based on the square-root cubature Kalman filter (SCKF) [9]
In [15], a distributed data association for multi-target tracking in sensor networks was proposed by Sandell et al In their paper, they considered that each sensor node can make noisy measurements of the target state
Summary
With the rapid development of image processing, sensor and semiconductor technology, the availability of inexpensive hardware, such as CMOS cameras, that are able to ubiquitously capture video content from the environment has fostered the development of camera networks [1]. Distributed algorithms have witnessed a surge in interest that has enabled a wide range of cooperation and information fusion in bandwidth-limited sensor networks They are advantageous for target tracking in camera networks due to their scalability and high fault tolerance [2,3]. The accuracy may not meet the requirements when they are used in the case of camera networks To solve these problems, this paper proposes a novel consensus filter based on the square-root cubature Kalman filter (SCKF) [9]. The main contribution of this paper is proposing data association with a square-root cubature information filter, taking special care of the issues of nonlinearity and finite word-length digital computers and using the proposed algorithm to track multi-targets in a camera network.
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