On vehicle state tracking for long-term carpark video surveillance
Published in Signal and Image Processing Applications (ICSIPA), 2017 IEEE International Conference, 2017
Recommended citation: Lim, R. W. S., Cheong, C. W., See, J., Tan, I. K., Wong, L. K., & Khor, H. Q. (2017, September). On vehicle state tracking for long-term carpark video surveillance. In Signal and Image Processing Applications (ICSIPA), 2017 IEEE International Conference on (pp. 368-373). IEEE. files/vehicle_icsipa17.pdf
Car park video surveillance systems present a huge volume of data that can be beneficial for video analytics and data analysis. We present a vehicle state tracking method for long term video surveillance with the goal of obtaining trajectories and vehicle states of various car park users. However, this is a challenging task in outdoor scenarios due to non-optimal camera viewing angle compounded by ever-changing illumination & weather conditions. To address these challenges, we propose a parking state machine that tracks the vehicle state in a large outdoor car park area. The proposed method was tested on 10 hours of continuous video data with various illumination and environmental conditions. Owing to the imbalanced distribution of parking states, we report the precision, recall and F1 scores to determine the overall performance of the system. Our approach proves to be fairly accurate, fast and robust against severe scene variations
Recommended citation:
Lim, R. W. S., Cheong, C. W., See, J., Tan, I. K., Wong, L. K., & Khor, H. Q. (2017, September). On vehicle state tracking for long-term carpark video surveillance. In Signal and Image Processing Applications (ICSIPA), 2017 IEEE International Conference on (pp. 368-373). IEEE.