Auto-resource provisioning for mapreduce-based multiple object tracking in video
Use of complex image analysis and globally optimal techniques make the current Multiple Object Tracking (MOT) methods for video analysis computationally slow. An important issue in this context is meeting the specific latency requirement for a given application while processing large scale video data. This is especially important in emergency situations such as accidents, natural calamities, and terrorist attacks. This paper introduces a latency reducing MapReduce/Hadoop-based parallel solution for MOT. The system includes an Auto-Resource Provisioning technique for determining the number of Hadoop nodes required to process the MOT job within a user specified deadline. The estimated number of nodes are then provisioned by the system and the MOT application is executed on the Hadoop cluster comprising the desired number of nodes. A prototype is built using the AWS EC2 cloud. A performance analysis is performed using measurements made on the prototype and insights gained into system behavior and performance are presented.
|Keywords||Video Processing, Multiple Object Tracking, Cloud Computing, Distributed Systems, MapReduce, Apache Hadoop|
|Conference||19th International Conference on Distributed Computing and Networking, ICDCN 2018|
Singh, G. (Gurinderbeer), Majumdar, S, & Rajan, S. (2018). Auto-resource provisioning for mapreduce-based multiple object tracking in video. In ACM International Conference Proceeding Series. doi:10.1145/3154273.3154340