Human detection and tracking with deep convolutional neural networks under the constrained of noise and occluded scenes

Human detection and tracking is a key aspect in surveillance system due to its importance in timely identification of person, recognition of human activity and scene analysis. Convolutional neural networks have been widely used approach in detection and tracking related tasks. In this paper, a robust framework is presented for the human detection and tracking in noisy and occluded environments with the aid of data augmentation techniques. In addition, a softmax layer and integrated loss function is used to improve the detection and classification performance of the proposed model. The primary focus is to perform the human detection task in unconstrained environments. The implemented system outperforms the state-of-the-arts methods which can be validated from the experimental results.