Pedestrian detection aided by scale discriminative network
Deep learning is greatly successful when used for pedestrian detection. However, we find that this method is barely satisfactory for multi scale detection. Meanwhile, various solutions such as multi scale classifiers have been developed (based on traditional methods) to handle this situation. Considering this, we propose a scale discriminative classifier layer (SDC) that contains numerous classifiers to cope with different scales. To expand the capacity for small scale pedestrian detection, we construct a full scale layer that converges both high level semantic features and low level features. From the analysis above, a scale discriminative network (SDN) for pedestrian detection was born. We apply this network to the Caltech pedestrian dataset, and the experimental results show that the SDN achieves state of the art performance.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)