Across-camera object tracking using a conditional random field model

To ensure safety, most public spaces now deploy monitoring systems. However, in most scenarios, the tracking operations of these monitoring systems are performed manually. These operations should be automated. This paper proposes using a conditional random field (CRF) to formulate the automatic execution problem as a cost minimization problem. The appearance of pedestrians and the time taken by them to cross the view of a camera are used to solve the automatic execution problem. Crowd psychology is used to define constraints and construct a CRF graph. A Siamese convolutional neural network is employed to recognize pedestrian appearance. The time spent by pedestrians crossing the view of a camera is modeled using a normal distribution. The results of two models are considered as the costs of nodes and edges. The proposed algorithm is applied under constraints to determine matches at the minimum cost. The accuracy of the proposed method is compared with that of other methods by using common datasets and benchmarks. Superior results are obtained when both appearance and spatiotemporal information are employed for solving the automatic execution problem than when using appearance alone.