  Every day the number of traffic cameras in cities rapidly increase and huge
amount of video data are generated. Parallel processing infrastruture, such as
Hadoop, and programming models, such as MapReduce, are being used to promptly
process that amount of data. The common approach for video processing by using
Hadoop MapReduce is to process an entire video on only one node, however, in
order to avoid parallelization problems, such as load imbalance, we propose to
process videos by splitting it into equal parts and processing each resulting
chunk on a different node. We used some machine learning techniques to detect
and track the vehicles. However, video division may produce inaccurate results.
To solve this problem we proposed a heuristic algorithm to avoid process a
vehicle in more than one chunk.
