Title :
Abnormal Event Detection at 150 FPS in MATLAB
Author :
Cewu Lu ; Jianping Shi ; Jiaya Jia
Author_Institution :
Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140-150 frames per second on average when computing on an ordinary desktop PC using MATLAB.
Keywords :
learning (artificial intelligence); least squares approximations; object detection; optimisation; video surveillance; MATLAB; detection phase; efficient sparse combination learning framework; ordinary desktop PC; short running time; small-scale least square optimization steps; speedy abnormal event detection; surveillance videos; MATLAB; Silicon; Surveillance; Testing; Training; Training data; Videos; abnormal event detection; dictionary learning; real-time; surveillance video;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, VIC
DOI :
10.1109/ICCV.2013.338