Real-time multiple object tracking in smart environments

We propose a real-time multiple object tracking method for smart environment applications. The proposed method combines identity features and tracking features for robust long-term object tracking. Face is a stable feature for identifying different subjects. However, face is not reliable for object tracking. Whole body color histogram is more resistant to object scale and pose changes, which has been widely applied in short-term object tracking. Unfortunately, color histograms are not stable over a relatively long period of time. In smart environments, same subjects may have different clothes over a long time span. We propose a method to combine these two features, the identity features and tracking features, to achieve reliable multiple object tracking in smart environments. A fast object labeling approach is proposed to track multiple objects in real time. The proposed method has clear advantage over traditional single feature methods. Experiments confirm that the proposed method can reliably track multiple human objects in real time through long video sequences.