Abstract
Tracking infrared pedestrian targets is a crucial part in video surveillance. Many factors make this problem decidedly non-linear and non-Gaussian, and the appropriate solution at present is based on the particle filter technique which is powerful and simple to implement. But in many cases, the traditional particle filter tracking algorithm fails to track the targets robustly and accurately. To solve these problems, a modified particle filter algorithm that combines intensity and edge cues is proposed. The algorithm firstly extracts the intensity cue and edge cue of the target based on the visual models which are originally learnt from the first frame and will be updated during the tracking process according to an automatic model updating strategy. Secondly, these two cues are combined into the particle filter framework by an adaptive integration scheme. Furthermore, its performance is evaluated with real-world infrared pedestrian sequences and extensive experimental results show that the presented method can track the infrared pedestrian more effectively and reliably than the traditional particle filter algorithm.
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