Robust Human Tracking Method Based on Appearance and Geometrical Features in Non- Overlapping Views

This paper proposes a robust tracking method which concatenates appearance and geometrical features to re-identify human in non-overlapping views. A uniformly-partitioning method is proposed to extract local HSV(Hue, Saturation, Value) color features in upper and lower portion of clothing. Then adaptive principal view selecting algorithm is presented to locate principal view which contains maximum appearance feature dimensions captured from different visual angles. For each appearance feature dimension in principal view, all its inner frames get involved in training a support vector machine (SVM). In matching process, human candidate filtering is first operated with an integrated geometrical feature which connects height estimate with gait feature. The appearance features of the remaining human candidates are later tested by SVMs to determine the object’s existence in new cameras. Experimental results show the feasibility and effectiveness of this proposal and demonstrate the real-time in appearance feature extraction and robustness to illumination and visual angle change.