This paper presents an updated iteration of the VERGE interactive video retrieval system. It offers various search options like free text and concept-based text search, color similarity, people and face detection, and visual and semantic similarity search. The system is designed to handle large amounts of data efficiently using advanced indexing techniques and state-of-the-art AI technology for visual content analysis. This paper describes enhancements made to improve usability for non-expert users, particularly through changes to the search and browsing interface.

Color comprises one of the most significant and dominant cues for various applications. As one of the most noticeable and stable attributes of vehicles, color can constitute a valuable key component in several practices of intelligent surveillance systems. In this paper, we propose a deep-learning-based framework that combines semantic segmentation masks with pixels clustering for automatic vehicle color recognition. Different from conventional methods, which usually consider only the features of the vehicle's front side, the proposed algorithm is able for view-independent color identification, which is more effective for the surveillance tasks. To the best of our knowledge, this is the first work that employs semantic segmentation masks along with color clustering for the extraction of the vehicle's color representative parts and the recognition of the dominant color, respectively. To evaluate the performance of the proposed method, we introduce a challenging multi-view dataset of 500 car-related RGB images extending the publicly available DSMLR Car Parts dataset for vehicle parts segmentation. The experiments demonstrate that the proposed approach achieves excellent performance and accurate results reaching an accuracy of 93.06% in the multi-view scenario. To facilitate further research, the evaluation dataset and the pre-trained models will be released at https://github.com/klearchos-stav/vehicle_color_recognition.

Surveillance systems currently deploy a variety of devices that can capture visual content (such as CCTV, body-worn cameras, and smartphone cameras), thus rendering the monitoring of video footage obtained from multiple such devices a complex task. This becomes especially challenging when monitoring social events that involve large crowds, particularly when there is a risk of crowd violence. This paper presents and demonstrates a crowd violence detection system that can process, analyze, and alert potential stakeholders, when violence-related content is identified in crowd-based video footage. Based on deep neural networks, the proposed end-to-end framework utilizes a 3D Convolutional Neural Network (CNN) to deal with the (near) real-time analysis of video streams and video files for crowd violence detection. The framework is trained, evaluated, and demonstrated using the Violent Flows dataset, a dataset related to crowd violence that is widely used for research. The presented framework is provided as a standalone application for desktop environments and can analyze both video streams and video files.

This work presents a spatio-temporal activity detection and recognition framework for untrimmed surveillance videos consisting of a three-step pipeline: object detection, tracking, and activity recognition. The framework relies on the YOLO v4 architecture for object detection, Euclidean distance for tracking, while the activity recognizer uses a 3D Convolutional Deep learning architecture employing spatio-temporal boundaries and addressing it as multi-label classification. The evaluation experiments on the VIRAT dataset achieve accurate detections of the temporal boundaries and recognitions of activities in untrimmed videos, with better performance for the multi-label compared to the multi-class activity recognition.

This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all cases, while also performing the violence detection activities in (near) real-time.