Abstract:

In the camera network with nonoverlapping FOVs (Field of Views), due to the factors such as the visual blind spot, it is difficult to track human continuously across multiple cameras. A human tracking method fusing the main color feature, textual feature and spatio-temporal topology feature is proposed. A SNNC (Sorted Nearest Neighbor Clustering) algorithm is adopted to extract the main color feature from the three human body parts which is head part, torso part, and legs part, and the matching rate is acquired. The spatial textual feature of the human are extracted to obtain the textural similarity. Combined with the two features above, the human appearance matching mode is constructed. Based on the statistic object correspondence information, the incremental learning method is exploited to construct and update the spatio-temporal information. The experiments prove that the proposed human tracking method can track the objects continually in camera network with non-overlapping FOVs. And the accuracy become higher over time as new observations are accumulated without supervised input.

Key words: multi-object tracking, non-overlapping field of views(FOVs), spatio-temporal information, objective correlation