Video based pedestrian detection and tracking at night-time
Geun-Hoo Lee, Gyu-Yeong Kim, Jong-Kwan Song, Ömer Faruk İnce, Jang‐Sik Park · 2017
This paper is an approach for pedestrian detection and tracking with infrared imagery. The detection phase is performed by AdaBoost algorithm based on Haar-like features. AdaBoost classifier is trained with datasets generated from infrared images. The number of negative images used for training with AdaBoost algorithm is 3000. For positive training, 1000 samples are used After detecting the pedestrian with AdaBoost classifier, we proposed the Tracking-Learning-Detection (TLD) frameworks tracking strategies. TLD frameworks are preferred in this study because of its high accuracy rate and computation speed Tracking performance comparison is made between TLD and particle filtering. Results prove that TLD performs a higher tracking rate than particle filtering.