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Detection of abandoned and stationary objects like luggage, boxes, machinery, and so forth, in public places is one of the challenging and critical tasks in the video surveillance system. These objects may contain weapons, bombs, or other explosive materials that threaten the public. Though various applications have been developed to detect stationary objects, different challenges, like occlusions, changes in geometrical features of things, and so forth, are still to be addressed. Considering the complexity of scenarios in public places and the variety of objects, a context‐aware model is developed based on mask region‐based convolution network (M‐RCNN) for detecting abandoned objects. A modified convolution operation is implemented in the Backbone network to understand features from geometric variations near objects. These modified operation layers can be adapted based on geometric interpretations to extract required features. Finally, a bounding box operation is performed to locate the abandoned object and mask the particular thing. Experiments have been performed on the benchmark dataset like ABODA and our dataset, which shows that an mAP of 0. 0.699 is achieved for model 1, 0.675 is achieved for model 2, and 0.734 mAP is completed for model 3. An ablation analysis has also been performed and compared with other state‐of‐the‐art methods. Based on the results, the proposed model better detects abandoned objects than existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

GOR stands for Groupe des Objets Revolutionaires, or, in English, the Group of Revolutionary Objects. This group was founded by Filipe Pais, Julie Brugier and Olivain Porry in 2018, in reaction to two major planetary concerns: the climate crisis and techno-solutionism. GOR, in response, opens a fictional design space for debate and aesthetic manifestations, wherein objects and machines are given autonomy, and the possibility of disobeying whilst challenging human decisions. This article is a statement about GOR's motivations, practice and research, written by two of the group members. In addition to further introducing the GOR manifesto and the forces which have given rise to it, it reviews its artistic practices and actions conducted during art residencies and group exhibitions; it identifies two examples of how the GOR design space can yield interesting clues in research, introducing the notion of super-object and an artistic framework named COCO2. [ABSTRACT FROM AUTHOR]

GOR stands for Groupe des Objets Revolutionaires, or, in English, the Group of Revolutionary Objects. This group was founded by Filipe Pais, Julie Brugier and Olivain Porry in 2018, in reaction to two major planetary concerns: the climate crisis and techno-solutionism. GOR, in response, opens a fictional design space for debate and aesthetic manifestations, wherein objects and machines are given autonomy, and the possibility of disobeying whilst challenging human decisions. This article is a statement about GOR’s motivations, practice and research, written by two of the group members. In addition to further introducing the GOR manifesto and the forces which have given rise to it, it reviews its artistic practices and actions conducted during art residencies and group exhibitions; it identifies two examples of how the GOR design space can yield interesting clues in research, introducing the notion of super-object and an artistic framework named COCO2.

This letter proposes an accurate static region classification for detecting abandoned or removed objects (ARO) using multiple cues. Most existing ARO detection approaches show many falsely detected static regions and low ARO detection performance in real situations because they use single cue and a number of pre-defined threshold values. The proposed method presents multiple cues as intensity, motion, and shape to characterize the true static regions and classifies their candidates into true/false static regions using a SVM classifier, which avoids any dependency on pre-defined threshold values. Experimental results show that the proposed method achieved better ARO detection accuracy and lower false detection rate than the existing methods. In addition, the proposed method can be utilized to several practical applications such as illegal parking detection, garbage throwing detection, thief detection, forest fire detection, and camouflaged solider detection. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner). [Copyright &y& Elsevier]

This paper describes a novel framework for a smart threat detection system that uses computer vision to capture, exploit and interpret the temporal flow of events related to the abandonment of an object. Our approach uses contextual information along with an analysis of the causal progression of events to decide whether or not an alarm should be raised. When an unattended object is detected, the system traces it back in time to determine and record who its most likely owner(s) may be. Through subsequent frames, the system searches the scene for the owner and issues an alert if no match is found for the owner over a given period of time. Our algorithm has been successfully tested on two benchmark datasets (PETS 2006 Benchmark Data, 2006; i-LIDS Dataset for AVSS, 2007), and yielded results that are substantially more accurate than similar systems developed by other academic and industrial research groups. [ABSTRACT FROM AUTHOR]

Increased communication capabilities and automatic scene understanding allow human operators to simultaneously monitor multiple environments. Due to the amount of data to be processed in new surveillance systems, the human operator must be helped by automatic processing tools in the work of inspecting video sequences. In this paper, a novel approach allowing layered content-based retrieval of video-event shots referring to potentially interesting situations is presented. Interpretation of events is used for defining new video-event shot detection and indexing criteria. Interesting events refer to potentially dangerous situations: abandoned objects and predefined human events are considered in this paper. Video-event shot detection and indexing capabilities are used for online and offline content-based retrieval of scenes to be detected. [ABSTRACT FROM PUBLISHER]