In this paper, a novel variational method is introduced for multi-object tracking in a network of cameras. In a camera network, objects are tracked by each camera using any of conventional algorithms and their tracks are extracted. Each extracted track is called a tracklet. The extracted tracklets are the inputs to our proposed method. Our objective in this paper is to associate the corresponding tracklets of an object and present the persistent trace of all objects. The association is formulated and solved using a variational energy function, which is based on appearance and motion model of objects. The optimization is realized by, first converting the variational energy function into an Ordinary Differential Equation (ODE) employing the Euler-Lagrange equation; then, the ODE is solved by numerical methods. The proposed method is evaluated on three well known real datasets and one synthetic dataset. The performance of our method is compared with the state of the art methods, employing the conventional metrics and under less restrictive assumption, and superiority of our method is demonstrated.
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