• 한국멀티미디어학회논문지
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• 제19권10호
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• pp.1782-1791
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• 2016

Various color spaces such as RGB, HSV, log-chromaticity have been used in the field of person re-identification. However, not enough studies have been done to find suitable color space for the re-identification. This paper reviews color invariance of color spaces by diagonal model and explores the suitability of each color space in the application of person re-identification. It also proposes a method for person re-identification based on a histogram refinement technique and some fusion strategies of color spaces. Two public datasets (ALOI and ImageLab) were used for the suitability test on color space and the ImageLab dataset was used for evaluating the feasibility of the proposed method for person re-identification. Experimental results show that RGB and HSV are more suitable for the re-identification problem than other color spaces such as normalized RGB and log-chromaticity. The cumulative recognition rates up to the third rank under RGB and HSV were 79.3% and 83.6% respectively. Furthermore, the fusion strategy using max score showed performance improvement of 16% or more. These results show that the proposed method is more effective than some other methods that use single color space in person re-identification.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.891-902
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• 2020

The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.785-796
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• 2019

Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.775-792
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• 2015

Person re-identification is an important and challenging task in computer vision with numerous real world applications. Despite significant progress has been made in the past few years, person re-identification remains an unsolved problem. This paper presents a novel appearance-based approach to person re-identification. The approach exploits region covariance matrix and color histograms to capture the statistical properties and chromatic information of each object. Robustness against low resolution, viewpoint changes and pose variations is achieved by a novel signature, that is, the combination of Log Covariance Matrix feature and HSV histogram (LCMH). In order to further improve re-identification performance, third-party image sets are utilized as a common reference to sufficiently represent any image set with the same type. Distinctive and reliable features for a given image set are extracted through decision boundary between the specific set and a third-party image set supervised by max-margin criteria. This method enables the usage of an existing dataset to represent new image data without time-consuming data collection and annotation. Comparisons with state-of-the-art methods carried out on benchmark datasets demonstrate promising performance of our method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2075-2092
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• 2017

Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.

• IEIE Transactions on Smart Processing and Computing
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• 제6권3호
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• pp.193-199
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• 2017

Instead of using only spatial features from a single frame for person re-identification, a combination of spatial and temporal factors boosts the performance of the system. A recurrent neural network (RNN) shows its effectiveness in generating highly discriminative sequence-level human representations. In this work, we implement RNN, three Long Short Term Memory (LSTM) network variants, and Gated Recurrent Unit (GRU) on Caffe deep learning framework, and we then conduct experiments to compare performance in terms of size and accuracy for person re-identification. We propose using GRU for the optimized choice as the experimental results show that the GRU achieves the highest accuracy despite having fewer parameters than the others.

• 한국빅데이터학회지
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• 제5권2호
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• pp.43-52
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• 2020

빅데이터 활용 기술의 발전으로 데이터의 저장 및 공유가 늘어나면서 그에 따른 프라이버시 침해가 일어나게 되었다. 이 문제를 해결하기 위해 비식별 기술이 도입되었지만 비식별된 데이터에 대해서도 재식별이 가능하다는 것이 여러 차례 증명되었다. 재식별 가능성이 존재하기 때문에 완전히 안전할 수 없지만 그럼에도 불구하고 충분한 비식별처리가 이루어져야 하는데, 현재 법령이나 규제는 어느 정도로 비식별 처리를 해야 하는지 정량적으로 규정하고 있지 않다. 본 논문에서는 재식별 작업을 할 때 소요되는 시간을 고려하여 적절한 비식별 기준을 제시하려고 한다. 다양한 비식별 평가 모델 중에서 k-익명성 모델에 대해 집중적으로 연구하였으며 어느 정도의 k값이 적절한 지 판단하였다. 본 연구의 결과를 일반화시킬 수 있다면 각종 법률 및 규제에서 적절한 비식별 강도를 규정하는 데 사용할 수 있을 것이다.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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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.

• 한국인터넷방송통신학회논문지
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• 제12권6호
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• pp.135-139
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• 2012

인터넷의 확산과 정보 교환, 배포와 수집 기술에 대한 의존도의 증대로 과거와는 비교할 수 없는 대용량의 데이터가 생성되었다. 대용량 데이터를 식별하고 가려내는 작업은 가까운 미래에 오늘날의 컴퓨터 과학의 상당 부분을 새롭게 정의할 것으로 예상된다. 여러 관련 분야에서 반복되는 중요한 과제는 재식별의 문제이다. 광범위한 정의에서, 재식별 문제는 과거에 인식된 객체를 다시 식별하는 문제이다. 예를 들면, 여러 장소에 설치된 감시 카메라에 포착된 어떤 사람을 추적하는 문제가 이에 해당한다. 본 논문에서는 서로 다른 분야에서 이 과제를 어떻게 정의하고, 이 과제를 어떻게 해결하는가에 대해 비교 분석한다. 비디오 감시에서 사람 재식별, 텍스트 샘플에서 저자 식별, 사진 선호도에 따른 사용자 식별 등이 이에 포함된다. 본 논문은 또한 학제간 해결 방안이 장점을 지니는 상황에 대한 비전을 제시한다.

• 한국콘텐츠학회논문지
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• 제11권4호
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• pp.404-413
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• 2011

최근 지역사회에 대한 관심 증대와 로컬리즘의 강화로 지역 간 경쟁이 치열해지면서 지역정체성에 대한 연구가 활발히 진행되고 있으며, 지역정체성에 대한 연구 분야 중 하나가 지역연고제를 바탕으로 운영되는 프로스포츠와의 관계에 대한 연구이다. 본 연구는 프로축구단을 대상으로 지역정체성이 팀정체성과 재관람의도에 미치는 영향을 규명하기 위해 수행되었다. 연구 대상은 D지역의 프로축구단 관람객이며 유의표본추출법에 의해 표집하고, 설문지를 통해 자료를 수집하였으며, 총 234명의 자료를 분석에 이용하였다. 자료처리는 SPSS 15.0과 AMOS 7을 이용하였고, 분석 방법은 빈도분석, 기술분석, 신뢰도분석, 상관분석, 확인적요인분석, 구조방정식 모형분석 등을 실시하였다. 연구결과, 지역정체성이 강할수록 해당 지역 연고팀에 대한 정체성이 높게 나타났다. 또한 팀정체성이 높을수록 재관람의도가 높은 것으로 나타났다. 본 연구의 결과는 프로스포츠팀의 운영에 있어서 연고지역의 정체성에 대한 이해와 연구가 필요하다는 점을 시사해준다.