be formulated as finding the best multiple paths in the graph to use the visual observations from an image sequence both motion and appearance models may be introduced given these models one may associate a weight to each edge defined between two nodes of the graph due to noisy foreground segmentation one target may report foreground regions and one foreground region may correspond to multiple targets to deal with the various issues several types of operations including merge split and reacquisition by appearance may be introduced if a prediction of one track at time t 1 has enough spatial overlapping with more than one observation at time t 1 a merge operation may generate a new observation when an observation at time t 1 is the best child of more than one track this may incur a split operation which splits a node into several new observations a reacquisition operation may be used to handle misdetection new hypotheses may carry a hypothesis proposed by merge split or reacquisition operation a final decision about tracking may be made by considering all of the observations in the graph the present multiple target tracking algorithm may be widely used in a visual surveillance application an input for the tracking algorithm may include the foreground regions and original image sequences or detected objects in the image a foreground region usually can be provided by a motion detection procedure an observation graph may be constructed which contains all of the observations within a time period an edge between nodes may be weighted by a joint motion and appearance likelihood the motion likelihood may be computed with a kalman filter the appearance likelihood may be the kl distance between two non parametric appearance models next one may perform an optimal path.