Deep Learning driven automated person detection and tracking model on surveillance videos

Abstract

Recently, person detection and tracking in a video scene of a surveillance system were grabbing higher interest because of its extensive range of applications in gender classification, abnormal event recognition, person identification, human gait characterization, individual counting in a dense crowd, and fall detection for older people, etc. Several methodologies were implied for person detection for surveillance applications. This paper presents a new DL driven automated person detection and tracking (DLD-APDT) model on surveillance videos. The major goal of the proposed DLD-APDT model is to identify people and track them in the videos. To accomplish this, the proposed DLD-APDT model initially performs frame conversion process where the input video is converted into a set of frames. In addition, the proposed DLD-APDT model employs EfficientDet model as object detector, i.e., persons and track them. EfficientDet is a recently developed object detector that makes use of different optimization and backbone tweaks, like bi-directional feature pyramid network (BiFPN) and a compound scaling approach. Moreover, root means square propagation (RMSProp) optimizer is applied to optimally choose the hyperparameters related to the EfficientNet model which helps in accomplishing enhanced performance. The experimental validation of the DLD-APDT model takes place using two training datasets namely PascalVOC and PenFudan datasets. The simulation results reported the enhanced detection and tracking performance of the DLD-APDT technique over recent approaches.

Keywords

• Object detection
• Object tracking
• Surveillance videos
• Deep learning
• Hyperparameter optimizer