With the widespread use of closed-circuit television (CCTV) surveillance systems in public areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent video surveillance system. It requires workforce and continuous attention to decide on the captured event, which is hard to perform by individuals. The available literature on human action detection includes various approaches to detect abnormal crowd behavior, which is articulated as an outlier detection problem. This paper presents a detailed review of the recent development of anomaly detection methods from the perspectives of computer vision on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection has been introduced. A summarization of existing reviews and datasets related to anomaly detection has been listed. It covers an overview of different crowd concepts, including mass gathering events analysis and challenges, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects have been analyzed.

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Taxonomy of Anomaly Detection Techniques in Crowd Scenes

This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Characteristic (ROC) curve and overall accuracy. Three benchmark datasets comprised of numerous video frames with various abnormal and normal actions were used to evaluate the performance. Experimental results revealed that the proposed model outperformed other baseline studies on all three datasets and achieved 98% AUC using the ROC curve. Moreover, the proposed model achieved 95.6%, 98%, and 97% AUC on the CUHK Avenue, UCSD Ped-1, and UCSD Ped-2 datasets, respectively.

/pdf/crowd-anomaly-detection-in-video-frames-using-fine-tuned-2ryhl3ud.pdf

Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model

Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes, we employed feature encoding and trajectory association governed by two metrices complementing to each other. The elements of an anomaly detection framework are optimized to run on CPU-only edge devices with sufficient frames per second (FPS). The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.

Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework.

Wearing masks in a crowded environment can reduce the risk of infection; however, wearing nonstandard cloud does not have a good protective effect on the virus, which makes it necessary to monitor the wearing of masks in real time. You only look once (YOLO) series models are widely used in various edge devices. The existing YOLOv5s method meets the requirements of inference time, but it is slightly deficient in terms of accuracy due to its generality. Considering the characteristics of our driver medical mask dataset, a position insensitive loss which is cloud extract shared area feature in different categories and half deformable convolution net methods with cloud concern noteworthy features were introduced into YOLOv5s to improve accuracy, with an increase of 6.7% mean average in @.5 (mAP@.5) and 8.3% in mAP@.5:.95 for our dataset. To ensure that our method can be applied in a real scenario, TensorRT and CUDA were introduced to reduce the inference time in two edge devices (Jetson TX2 and Jetson Nano) and one desktop device, whose inference time was faster than that of previous methods.

https://downloads.hindawi.com/journals/cin/2022/6170245.pdf

PIS-YOLO: Real-Time Detection for Medical Mask Specification in an Edge Device

Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feeds from surveillance cameras. Object detection is the most important step in these systems, and much research has been done on identifying objects in traffic scenes. This paper reviews various algorithms used for object detection in traffic surveillance, in addition to the recent trends and future directions. Based on the approaches used in the related studies, the object detection methods are categorized into motion-based and appearance-based techniques. Each group of techniques is further classified into a number of subcategories and the advantages and disadvantages of each method are finally analyzed. The major challenges, limitations, and potential solutions are also discussed along with the future directions. 

https://www.techrxiv.org/articles/preprint/Object_Detection_in_Traffic_Videos_A_Survey/20477685/1/files/36694116.pdf

Object Detection in Traffic Videos: A Survey

The current concept of Smart Cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and give a decent quality of life to its residents. To fulfill this need video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we surveyed various methodologies developed to detect anomalies in intelligent video surveillance. Firstly, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies developed for ease of understanding. Considering the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is considered a time-critical application of computer vision, our emphasis is on anomaly detection using edge devices and approaches explicitly designed for them. Further, we discuss the challenges and opportunities involved in anomaly detection at the edge.

Mayur Rajram Parate

Papers

Anomaly Detection using Edge Computing in Video Surveillance System: Review