ISSN 0236-235X (P)
ISSN 2311-2735 (E)

Journal influence

Higher Attestation Commission (VAK) - К1 quartile
Russian Science Citation Index (RSCI)


Next issue

Publication date:
17 March 2024

Journal articles №1 2020

21. Intelligent analysis video data to recognize car theft situations in a parking lot [№1 за 2020 год]
Authors: A.Yu. Kruchinin ( - Orenburg State University (Associate Professor), Ph.D; R.R. Galimov ( - Orenburg State University (Associate Professor), Ph.D;
Abstract: Modern video surveillance systems are very popular to prevent and investigate various illegal inci-dents. The main disadvantage of traditional decisions in this area is that the operator directly assesses the current situation and makes a decision. With a large number of monitored cameras and due to the human factor, there is a high probability of increasing the time delay in determining the dangerous situ-ation. This leads to significant damage, especially in cases where it is necessary to respond promptly to an incident. Such cases include, for example, the situation of carjacking. These circumstances necessi-tate the introduction of systems for the intellectual analysis of video data. This paper submits a method for recognizing situations of car theft from parking based on stochastic grammars and deep neural networks. Recognition of an incident is at two main levels: at the lower lev-el, event recognition occurs, at the upper level, the situation as the most probable chain of events cor-responding to the grammar of car theft signatures. The analysis of the identified objects, their relative position, the dynamic characteristics of the trajectory of movement and the characteristics of the peo-ple’s postures, fills up the detection of possible hijacking events. The deep neural networks recognize the objects and people poses. These networks have a high degree of reliability. The article developed a simulation model of the system of recognition of the situations of theft of road transport, which based on the object detection module using a deep neural network. The history of certain objects in previous frames and, if necessary, data on the posture of a person improves the as-sessment of the reliability of event recognition. To describe the possible scenarios of car theft, the authors developed a stochastic grammar and cre-ated the test utility based on it. The test results of the developed method on the Mini-deone video da-taset showed its efficiency.
Keywords: hijacking recognition, deep neural network, stochastic grammars, event-situational approach, human posture recognition
Visitors: 2520

← Preview | 1 | 2 | 3