WatchNet++: efficient and accurate depth-based network for detecting people attacks and intrusion

Abstract

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not able to distinguish people from other elements in the scene, we propose WatchNet++ that is a depth-based and sequential network that localizes people in top-view depth images by predicting human body joints and pairwise connections (links) such as head and shoulders. WatchNet++ comprises a set of prediction stages and up-sampling operations that progressively refine the predictions of joints and links, leading to more accurate localization results. In order to train the network with varied and abundant data, we also present a large synthetic dataset of depth images with human models that is used to pre-train the network model. Subsequently, domain adaptation to real data is done via fine-tuning using a real dataset of depth images with people performing attacks and intrusion. An extensive evaluation of the proposed approach is conducted for the detection of attacks in airlocks and the counting of people in indoors and outdoors, showing high detection scores and efficiency. The network runs at 10 and 28 FPS using CPU and GPU, respectively.