A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems
It addresses performance bottlenecks for researchers and practitioners in video analytics, but is incremental as it builds on existing surveys by shifting focus.
This survey paper tackles the problem of optimizing performance in neural network-based video analytics systems, reviewing techniques that focus on efficiency rather than accuracy.
Video analytics systems perform automatic events, movements, and actions recognition in a video and make it possible to execute queries on the video. As a result of a large number of video data that need to be processed, optimizing the performance of video analytics systems has become an important research topic. Neural networks are the state-of-the-art for performing video analytics tasks such as video annotation and object detection. Prior survey papers consider application-specific video analytics techniques that improve accuracy of the results; however, in this survey paper, we provide a review of the techniques that focus on optimizing the performance of Neural Network-Based Video Analytics Systems.