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Video surveillance systems are commonly used by security personnel to monitor and record activity in buildings, public gatherings, busy roads, and parking lots. These systems allow many cameras to be observed by a small number of trained human operators but suffer from potential operator fatigue and lack of attention due to the large amount of information provided by cameras which can distract the operator from focusing on important events. In this paper, we propose the design of an autonomous video surveillance system which can operate from a standoff range that analyzes approaching vehicles in order to detect security anomalies. Such anomalies, based on dynamic analysis of the vehicle tracks, include unexpected slowing/stopping or sudden acceleration, particularly near check points or critical structures (e.g. government buildings). A human supervisor can be alerted whenever a significant event is detected and can then determine if the vehicle should be further inspected. Besides dynamic analysis, the system also estimates physical information about the vehicles such as make, body type and tire size. We describe low-complexity techniques to obtain the above information from two cameras.