• Title/Summary/Keyword: Large-scale Video Surveillance System

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A Surveillance Algorithm Selection Method Based on Video Features for Large-scale Integrated Surveillance Systems (대규모 종합감시시스템 환경에서의 비디오의 특징분석 기반 감시 알고리즘 선택 기법)

  • Park, Kwang-Young;Park, Goo-Man
    • Journal of Satellite, Information and Communications
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    • v.7 no.1
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    • pp.33-38
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    • 2012
  • In this paper we proposed an algorithm selection method based on the features of video inputs which are acquired from the large scale integrated surveillance system. The example of integrated surveillance system is the metro railway system. Automated surveillance system at large area saves the human resource and minimizes the non-observing spots. We have analyzed the input video under this system in order to apply adequate video analytics algorithms in each installing places and for each situation. Based on the analysis, we suggested event processing scenarios and video analytic algorithm selection.

Required Video Analytics and Event Processing Scenario at Large Scale Urban Transit Surveillance System (도시철도 종합감시시스템에서 요구되는 객체인식 기능 및 시나리오)

  • Park, Kwang-Young;Park, Goo-Man
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.11 no.3
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    • pp.63-69
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    • 2012
  • In this paper, we introduced design of intelligent surveillance camera system and typical event processing scenario for urban transit. To analyze video, we studied events that frequently occur in surveillance camera system. Event processing scenario is designed for seven representative situations(designated area intrusion, object abandon, object removal in designated area, object tracking, loitering and congestion measurement) in urban transit. Our system is optimized for low hardware complexity, real time processing and scenario dependent solution.

Face Detection Using Multi-level Features for Privacy Protection in Large-scale Surveillance Video (대규모 비디오 감시 환경에서 프라이버시 보호를 위한 다중 레벨 특징 기반 얼굴검출 방법에 관한 연구)

  • Lee, Seung Ho;Moon, Jung Ik;Kim, Hyung-Il;Ro, Yong Man
    • Journal of Korea Multimedia Society
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    • v.18 no.11
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    • pp.1268-1280
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    • 2015
  • In video surveillance system, the exposure of a person's face is a serious threat to personal privacy. To protect the personal privacy in large amount of videos, an automatic face detection method is required to locate and mask the person's face. However, in real-world surveillance videos, the effectiveness of existing face detection methods could deteriorate due to large variations in facial appearance (e.g., facial pose, illumination etc.) or degraded face (e.g., occluded face, low-resolution face etc.). This paper proposes a new face detection method based on multi-level facial features. In a video frame, different kinds of spatial features are independently extracted, and analyzed, which could complement each other in the aforementioned challenges. Temporal domain analysis is also exploited to consolidate the proposed method. Experimental results show that, compared to competing methods, the proposed method is able to achieve very high recall rates while maintaining acceptable precision rates.

Implementation of a Video Distribution Server to Enhance QoS of Network Cameras for the Video Surveillance System (영상 감시용 네트워크카메라의 서비스 품질 향상을 위한 영상분배서버 구현)

  • Jeong, Tae-Young;Yim, Kang-Bin
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.45 no.9
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    • pp.67-74
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    • 2008
  • This paper proposes, designs and implements an architecture of a server involved with the network camera based video surveillance systems to solve common problems including lack of inter-network operability at the video information sharing, drawback of bandwidth and processing-overhead caused by increase of the number of users, and difficulty of continuous monitoring over changes of network configurations. The proposed saver was designed to manage and service numerous network cameras and users as well as solving the existing problems by providing video distribution facility. Through the empirical study after applying the implemented server to a real video surveillance system we proved that the server can provide reasonable service quality while it processes several hundreds of simultaneous user connections under persisting more than one hundred connections to network cameras. We expect the developed video distribution server to enhance service quality of the large scale video surveillance systems for citizen-wide services such as traffic reporting informatics or natural calamities supporting.

Person Re-identification using Sparse Representation with a Saliency-weighted Dictionary

  • Kim, Miri;Jang, Jinbeum;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.6 no.4
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    • pp.262-268
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    • 2017
  • Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity

  • Shahabi, Cyrus;Kim, Seon Ho;Nocera, Luciano;Constantinou, Giorgos;Lu, Ying;Cai, Yinghao;Medioni, Gerard;Nevatia, Ramakant;Banaei-Kashani, Farnoush
    • Journal of Information Processing Systems
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    • v.10 no.1
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    • pp.1-22
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    • 2014
  • Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

Object Tracking Based on Exactly Reweighted Online Total-Error-Rate Minimization (정확히 재가중되는 온라인 전체 에러율 최소화 기반의 객체 추적)

  • JANG, Se-In;PARK, Choong-Shik
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.53-65
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    • 2019
  • Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.