No abstract

Crime prediction in video-surveillance systems is required to prevent incident and protect assets. In this sense, our article proposes first artificial intelligence approach for Robbery Behavior Potential (RBP) prediction and detection in an indoor camera. Our method is based on three detection modules including head cover, crowd and loitering detection modules for timely actions and preventing robbery. The two first modules are implemented by retraining YOLOV5 model with our gathered dataset which is annotated manually. In addition, we innovate a novel definition for loitering detection module which is based on DeepSORT algorithm. A fuzzy inference machine renders an expert knowledge as rules and then makes final decision about predicted robbery potential. This is laborious due to: different manner of robber, different angle of surveillance camera and low resolution of video images. We accomplished our experiment on real world video surveillance images and reaching the F1-score of 0.537. Hence, to make an experimental comparison with the other related works, we define threshold value for RBP to evaluate video images as a robbery detection problem. Under this assumption, the experimental results show that the proposed method performs significantly better in detecting the robbery as compared to the robbery detection methods by distinctly report with F1-score of 0.607. We strongly believe that the application of the proposed method could cause reduction of robbery detriment in a control center of surveillance cameras by predicting and preventing incident of robbery. On the other hand, situational awareness of human operator enhances and more cameras can be managed.

Face detection is a crucial task in computer vision and image processing with various practical applications including security, surveillance and entertainment. In recent years, approaches based on deep learning methods improved significantly with high-accuracy detection results. In this paper, we propose a new framework for anefficient face detection based on improved EfficientDet architecture and wavelet transform. Our method utilizes a combination of innovated bi-directional feature pyramid network (BiFPN) and a dual-tree complex wavelet transform called WT-BiFPN to ameliorate feature representation of faces at multiple scales. We evaluate our approach on two benchmark datasets, including WIDER FACE and our gathered dataset with specific details close to real-world images. Our proposed architecture achieves more than 5% performance improvement on previous state-of-the-art EfficientDet-based methods in terms of mean average precision (mAP). Our method provides an effective accurate face detection solution for several applications and can prevail over low resolution and occlusion in images.