This paper proposes a boosting-like deep learning (BDL) framework for pedestrian detection. The fusion of handcrafted and deep learned features is considered to extract more effective representations. Due to overtraining on the limited training samples, over-fitting and convergence stability are two major problems of deep learning. We propose the boosting-like algorithm to enhance the system convergence stability through adjusting the updating rate according to the classification condition of samples in the training process. We theoretically give the derivation of our algorithm. Our approach achieves 15.85% and 3.81% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech dataset, respectively.
['Lei Wang', 'Baochang Zhang', 'Wankou Yang']
Boosting-Like Deep Convolutional Network for Pedestrian Detection