KEYWORDS: Video, Video surveillance, Sensors, Scene classification, Environmental sensing, Detection and tracking algorithms, Fermium, Frequency modulation, Environmental monitoring, Image segmentation
Change detection is the most fundamental component in video surveillance systems. Although many change detection approaches have been proposed, they are often only suitable for particular environments. This paper presents an approach that integrates several detection techniques with scene understanding capability, thereby overcoming the challenges of detecting various scene types and improving overall detection performance. First, a scene-awareness algorithm that incorporates a deep learning-based scene recognition model and support vector machine is developed to classify the monitored scene over time. Then, the appropriate detection technique is automatically adopted to perform scene-specific detection. Experimental results demonstrate that the performance of the proposed method is comparable to that of the state-of-the-art methods and satisfies the requirements of real-time practical applications. Hence, it can serve as an intelligent change detection approach for visual analytics in video surveillance systems.
Public safety is a matter of national security and people’s livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method’s performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.
Moving foreground detection can be used for the intelligent surveillance system and computer vision as an important step for many applications. Previous researchers have developed many different moving foreground detection technologies, such as background subtraction and optical flow. However, as far as we knew, there was few literature investigated ensemble method in integrate with various foreground detection technologies in real-time. In this paper, we present a new approach inspired from the ensemble system of machine learning to detect moving foreground by using weighted matrix with spatial characteristics. Furthermore, the weighted values can be automatically scaled over time for optimal flexibility and parameterization in our method. The experimental results demonstrate that the proposed method can not only provide compared performance with the state-of-the-art methods, but also satisfy real-time applications.