Multi-Task Network Combing Multi-Level Information for Object Localization

Object localization is a very important challenge in computer vision. At present, region convolutional neural networks（RCNN） and extensions had accuracies limited by the size of the output convolution map, hence they could not get exact object locations. In this paper, a new algorithm based on multi-task convolutional neural network was proposed for object localization, firstly different tasks shared feature extraction layer, and then the sub-networks for different levels of information were trained. When the high-level and low-level information were extracted, the accurate target location was obtained by a fusion of these two level information. Finally, the performance of the algorithm was verified on the PASCAL VOC 2007 database and the traffic scene database, and experiments show that the algorithm can effectively improve the accuracy of object localization.