A Study on CCTV-Based Dangerous Behavior Monitoring System

Transcription

1 , pp A Study on CCTV-Based Dangerous Behavior Monitoring System Young-Bin Shim 1, Hwa-Jin Park 1, Yong-Ik Yoon 1 1 Dept. of Multimedia, Sookmyung Women s University, Seoul, Korea { rindasim, phj2000,yiyoon}@sookmyung.ac.kr Abstract. Proposed in this paper is a detection system that, by determining whether a dangerous act is being carried out in the images captured using CCTV, provides pre-warnings and establishes emergency measures. To determine the presence of a dangerous act, after setting zones of interest and danger zones within those zones of interest, the danger is detected when an object trespasses the danger zone. If a situation is deemed as warning-worthy and emergency, the integrated control center is immediately alerted to facilitate prevention in advance. Keywords: CCTV, blob segmentation, object detection, determination of danger level 1 Introduction Recently, due to a sharp increase in sexual offenses that threaten social safety or due to suicide incidents caused by stresses such as anxiety and depression, there has been a rapid rise in interest in social safety systems. In particular, since most of the cases occur in deserted areas or during times of darkness, relatively low-cost CCTVs have been installed and used, instead of manning via personnel, for monitoring and preparation. However, even though these precautions have played an important part in post-incident investigations, unless someone continues to monitor in real-time CCTV systems do not help to prevent incidents at all. As such, this study is purposed in studying a danger detection system that is not intended for providing post-incident reference but for providing pre-incidence prevention; to achieve such, the system would examine the danger level by detecting a person s dangerous act and, in dangerous cases, it would alert the emergency situation with beeping or via the system. Situations and criteria are very diverse and wide for detecting all dangerous acts and the technical difficulty of image analysis varies depending on the number of This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2013S1A5B ) ISSN: ASTL Copyright 2013 SERSC

2 target persons involved; therefore, in this study, specific dangerous acts were preclassified according to the number of target persons. Table 1. Dangerous acts in accordance with the number of target persons. Number of Main Examples of Dangerous Acts Target Persons 1 Person Suicide, Self-infliction, Smoking in non-smoking area, Unlawful entry 2 Persons Fighting, Making threats, Sexual attack, Assault, Stalking, Assault on children 3 Persons or more Group assault, etc. 2 Dangerous Action Detection System 2.1 Overall Process The system this study is attempting to implement is a CCTV-based system for detecting bridge suicide acts and it has the following characteristics. It is always in a fixed state as long as the position and angle are not changed. Utilizing this characteristic, the overall process of this system can be summarized as follows. First, on the bridge CCTV footage the area of interest for detection is set and cut, and within the area of interest, danger zones are selected via the coordinates. The overall process is composed of image segmentation, object detection, and danger detection. Fig 1. shows a flow diagram of object and danger detection system. Fig. 1. Object detection and danger determination system flow 2.2 Image Segmentation Unit The background image in a CCTV-based bridge suicide detection system is always constant once the CCTV has been installed. The background image is extracted from the rows of sequential images. We set ROI (region of interest) in order to exclude 96 Copyright 2013 SERSC

3 roads with vehicle traffic or areas with nearby buildings and to measure the status of the area of interest which is drawn in dashed line in Fig 2. In addition, we set the danger zone by selecting the river water portion, which is the monitoring zone within the interested area, which is drawn in solid line in Fig 2. Through the differential calculations of the background image extracted in such a way and the current image, the AVD (absolute value difference) frame is generated. Although through ROI settings the unwanted parts of the background were removed, the wind-induced shaking or river water current movements were detected. To solve this problem, several measurements were taken, and by assigning appropriate threshold values of 20~30 that would exclude water current movements and determine an object and by binarization, images for object detection were obtained. (a) (b) (c) (d) Fig. 2 (a) Background frame, (b) Current frame, (c) AVD frame (d) Binarization 2.3 Object Detection Unit As shown earlier, there are a variety of methods for extracting objects in an image. In this study, by performing a morphological filtering of the original image, a morphology dilation calculation, on the acquired image, the empty spaces in the object are filled to make the object more clear, and then, by connecting the object s contours to find the object, and after that, unique labeling is done on each of the next objects thereby distinguishing each object. Copyright 2013 SERSC 97

4 2.4 Danger Detection Unit The reasoning for determining a suicide jump from the bridge is relatively simple. With the bridge railing as the center, if an object inside the safe zone of pedestrian path moves to the water below the bridge, i.e., danger zone, then it can be deemed dangerous. Thus, the danger is detected when an object trespassing the danger zone. (a) (b) Fig. 3. Alarm operating condition in (a) Safe state and (b) Emergency state 3 Experiment Results and Analysis The color difference method which is used in this study encompasses the issue of the results being different according to the illumination variation and the colors of the background and object that are dependent on the weather and times. As is described in Chapter 4, the problem in the part of object detection is one of the future research topics, and if movement-related characteristics are to be combined, the object size error problem caused by colors being similar to the background colors is expected to be reduced. In this chapter, in order to determine the accuracy of the system, assuming first that the object size was being accurately measured, the accuracy of measurement of the danger level was analyzed according to object size. As a result, it was seen that the results according to various positions of the object were accurate when the size of object is larger than 800. The result is shown in Table 2. A rather low accuracy in small size is due to other objects such as a car in the ROI. Therefore, if the street is eliminated from the ROI, it was ascertainable that the results came out accurately even when the object size varies due to the object distance. 4 Conclusion and Future Challenges This research is a study that implemented a system alerts the control room about the primary alarm and emergency situations by automatically detecting dangerous situations via detecting dangerous acts, i.e., suicide jumps from the bridge, in CCTV-. 98 Copyright 2013 SERSC

5 Table 2. Success rate of object size versus different states. Object Size(Area) Safe State Emergency State Small (less than 800) Medium (800 ~ 2500) Large (larger than 2500) 75% 87.5% 100% 87.5% 100% 100% based images. This study is a basic stage where the target object was limited to 1 person. As future research topics, challenges of black-and-white detection using infrared CCTVs for detecting dangerous acts at nighttime and, additionally, studies of dangerous act detection systems that monitor not only suicide jumps but also preactions related to suicide, such as pacing, cigarette smoking, etc. involving more than 1 person, may be considered. The ultimate goal would be to develop a system that detects various dangerous actions, e.g., groups assaults, related to cases involving more than 3 persons. References 1. T. Chen, H. Haussecker, A. Bovyrin, R. Belenov, K. Rodyushkin, A. Kuranov and V. Eruhimov. Computer vision workload analysis: case study of video surveillance systems. Intel Technology Journal, vol. 9, no 2, pp , (May. 2005) 2. I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: Real-time surveillance of people and their activities," IEEE Trans. Pattern Anal. and Mach. Intel., Vol. 22, no. 8, pp , (Aug. 2000) 3. F. Porikli, "Detection of temporarily static regions by processing video at different frame rates," in Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance, pp , London, UK (2007) 4. P. L. Venetianer, Z. Zhang, W. Yin, A. J. Lipton, P. L. Venetianer, Z. Zhang, W. Yin, and A. J. Lipton, "Stationary target detection using the object video surveillance system," in Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance, pp , London, UK (2007) 5. M. L. Comer and E. J. Delp, "Morphological operations for color image processing," J. Electron. Imaging, vol. 8, no. 3, pp , (Jul. 1999) 6. Wang Junqing, Shi Zelin, and Huang Shabai, "Detection of Moving Targets in Video Sequences." Opto-Electronic Engineering, pp. 5-8, (Dec. 2005) 7. A.Saha, J.Mukherjee, and S. Sural, "New pixel-decimation patterns for block matching in motion estimation," Signal Processing: Image Communication, vol.23, no.10, pp , (2008) Copyright 2013 SERSC 99