Beyond counting: point supervised attention guided neural network for crowded object locating
Recent advances in convolutional neural networks have achieved prominent results for object counting or locating in images. In these networks, some adopt detection based methods which require providing bounding box annotations for training and these methods usually cannot deal with the crowded cases well. Other networks adopt density regression based methods which require only point annotations for training but these methods can only predict the total count without providing any location information. In this Letter, the authors propose an attention guided convolutional neural network (AGNet) for both crowded object locating and counting using point annotation as supervision without annotated bounding boxes which are far more time consuming to label. Accordingly, they define a multi task objective loss function to lead the training of AGNet to achieve the goal of crowded object locating. The experimental results on four challenging benchmark datasets for crowded objects counting demonstrate the superiority of the proposed AGNet over recent state of the art approaches.
Electronics Letters