Recognizing People in Non-Intersecting Camera Views

Many studies have now confirmed that it is possible to recognize people by the way they walk. As yet there has been little formal study of identity tracking using gait over different camera views. We present a new approach for people tracking and identification between different non-intersecting uncalibrated cameras based on gait analysis. An identification signature is derived from gait kinematics as well as anthropometric knowledge. Given the nature of surveillance data, we have developed a new feature extraction technique for finding human legs. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirm the robustness of our method to extract gait features in different scenarios. Furthermore, experimental results revealed the potential of our method to work in real surveillance systems to recognize walking people over different views with achieved cross-camera recognition rates of 95% and 90% for two different views.