Divide and Conquer: an Accurate Machine Learning Algorithm to Process Split Videos on a Parallel Processing Infrastructure
This work addresses inefficiencies in traffic camera video processing for urban monitoring systems, but it is incremental as it builds on existing parallel processing methods.
The paper tackles the problem of load imbalance in parallel video processing by splitting videos into equal parts for different nodes, and proposes a heuristic algorithm to prevent vehicles from being processed across multiple chunks, achieving accurate vehicle detection and tracking.
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.