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As it requires a huge number of parameters when exposed to high dimensionalinputs in video detection and classification, there is a grand challenge todevelop a compact yet accurate video comprehension at terminal devices. Currentworks focus on optimizations of video detection and classification in aseparated fashion. In this paper, we introduce a video comprehension (objectdetection and action recognition) system for terminal devices, namely DEEPEYE.Based on You Only Look Once (YOLO), we have developed an 8-bit quantizationmethod when training YOLO; and also developed a tensorized-compression methodof Recurrent Neural Network (RNN) composed of features extracted from YOLO. Thedeveloped quantization and tensorization can significantly compress theoriginal network model yet with maintained accuracy. Using the challengingvideo datasets: MOMENTS and UCF11 as benchmarks, the results show that theproposed DEEPEYE achieves 3.994x model compression rate with only 0.47% mAPdecreased; and 15,047x parameter reduction and 2.87x speed-up with 16.58%accuracy improvement.

DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization