Human identity recognition in aerial images

Oreifej, Omar; Mehran, Ramin; Shah, Mubarak

doi:10.1109/cvpr.2010.5540147

Cited by 118 publications

(111 citation statements)

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“…Besides, the authors extracted blobs using the method called maximally stable colour regions (MSCR) in [7]. In [24], the authors detected blobs on person images. Then, they extracted color histogram and histogram of oriented gradient (HOG) for visual features.…”

Section: Related Workmentioning

confidence: 99%

Person re-identification with mutual re-ranking

Nguyen

Ngo

et al. 2017

Vietnam J Comput Sci

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Person re-identification is the problem of identifying people moving across cameras. Traditional approaches deal with this problem by pair-wise matching images recorded from two different cameras. A person in the second camera is identified by comparing his image with images in the first camera, independently of other persons in the second camera. In reality, there are many situations in which multiple persons appear concurrently in the second camera. In this paper, we propose a method for postprocessing re-identification results. The idea is to utilize information of co-occurrence persons for comparing and re-arranging given ranked lists. Experiments conducted on different datasets with several state-of-the-art methods have shown the effectiveness of our post-processing method in improving re-identification accuracy.

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Section: Related Workmentioning

confidence: 99%

Person re-identification with mutual re-ranking

Nguyen

Ngo

et al. 2017

Vietnam J Comput Sci

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“…9 shows accuracy of pedestrian recognition between our KMFS-WRM algorithm and Oreifej's original WRM algorithm [11]. There are three experimental results of different outdoor scene, including daytime soccer field, nighttime basketball court and square: the first row is the result of KMFS-WRM algorithm in each group, and the second row is the result of Oreifej's original WRM algorithm [11]. The first column in Fig.…”

mentioning

confidence: 99%

“…The third group of images is binary images by operating on the second group of gray images. The fourth group of images is segmented by traditional segmentation algorithm [11]. We find the result of segmentation is worse, and also many regions of pedestrian are occluded.…”

mentioning

confidence: 99%

“…Thirdly we segment the pedestrian using context-aware saliency detection algorithm [13] to relieve the effect of changing shape, resolution and appearance feature. Last we recognize the pedestrian by the WRM method [11]. These aforementioned steps are explained in the following subsections.…”

mentioning

confidence: 99%

“…9. The comparison of pedestrian recognition between the KMFS-WRM and Original WRM (total 3 groups, the first row is the result of KMFS-WRM and second row is Original WRM, red line with arrow is right and blue line with arrow is wrong).In addition, we do 100 times experiments respectively using KMFS-WRM algorithm and original WRM [11] in every environmental video (grass, football field and square). Meanwhile, we record the consuming time in matching process and final result of recognition in each experiment.…”

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confidence: 99%

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Pedestrian Recognition in Aerial Video Using Saliency and Multi-Features

Liu¹,

Xu²,

Wang³

et al. 2014

IJCEE

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Abstract-Pedestrian recognition in aerial video is a challenge problem for the problem of low resolution, camera movement and target's blurred detail in aerial video. This paper proposes weighted region matching algorithm with Kalman filter, Multi-features fusion model and saliency segmentation (KMFS-WRM) to detect and recognize pedestrian. The KMFS-WRM algorithm first uses Kalman filter algorithm to mark candidate's region, which can avoid the problem of selecting candidates under supervision. Then we proposed the fusion algorithm of multi-feature, including HOG, LBP and SIFT features, namely HLS model to detect the pedestrian in aerial video. Our proposed detection method is robust for whether the camera is moving. And instructing human percept and concept, we segment the pedestrians in marked region using Context-Aware saliency detection algorithm that proposed by Goferman et al. and revised the segmentation results by HST model (Head Shoulder and Torso) and AAM model (Active Appearance Model) to obtain the candidates set. Last the matching of voter and candidates set using weighted region matching algorithm. Experimental results in complex aerial video demonstrated that our KMFS-WRM algorithm not only cuts down calculated complexity, but also improves adaptive and real-time ability. Moreover proposed method outperforms recent state-of-the-art methods.Index Terms-Pedestrian detection, saliency detection, Kalman filter algorithm, weight region matching. I. INTRODUCTIONPedestrian detection and recognition is an essential and significant task in any intelligent video surveillance system, since it provides the fundamental semantic information of video understanding. Along with the urgent demand for video surveillance systems in many security-sensitive occasions, e.g., some large squares, stadiums, super-markets et al., the research on non-contact pedestrian detection and recognition draws more attention. Most existing methods of pedestrian detection and recognition mainly depend on the extraction of features based on image details [1]- [4]. As one of main information source for non-contact pedestrian detection and recognition, aerial video has seen widely used in both military and commercial world application where its advantages over traditional video outweigh its disadvantages, such as poorer Manuscript received December 19, 2013; revised May 5, 2014. This work was supported in part National Science Foundation of China (NSFC Grant No. 61272258, 61170124, 61170020, 61301299).Chunping Liu, Xu Fang, and Shengrong Gong are with School of Computer Science and Technology, Soochow University, Suzhou 215006, China (e-mail: cpliu@suda.edu.cn, fangxu_8040595@126.com, shrgong@suda.edu.cn).Xingbao Wang was with School of Computer science and technology, Soochow University, Suzhou 215006, China. He is now with Anhui USTCiflytek Co., LTD (e-mail: wangxingbao@163.com).spatial resolution, moving video camera, targets with much smaller and more obscure details, complex background and high density pedestrian flow. Therefore, in...

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Real‐time surveillance detection system for medium‐altitude long‐endurance unmanned aerial vehicles

Amanatiadis

Bampis

Γαστεράτος

2017

Concurrency and Computation

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Summary The detection of ambiguous objects, although challenging, is of great importance for any surveillance system and especially for an unmanned aerial vehicle, where the measurements are affected by the great observing distance. Wildfire outbursts and illegal migration are only some of the examples that such a system should distinguish and report to the appropriate authorities. More specifically, Southern European countries commonly suffer from those problems due to the mountainous terrain and thick forests that contain. Unmanned aerial vehicles like the “Hellenic Civil Unmanned Air Vehicle” project have been designed to address high‐altitude detection tasks and patrol the borders and woodlands for any ambiguous activity. In this paper, a moment‐based blob detection approach is proposed that uses the thermal footprint obtained from single infrared images and distinguishes human‐ or fire‐sized and shaped figures. Our method is specifically designed so as to be appropriately integrated into hardware acceleration devices, such as General Purpose Computation on Graphics Processing Units (GPGPUs) and field programmable gate arrays, and takes full advantage of their respective parallelization capabilities succeeding real‐time performances and energy efficiency. The timing evaluation of the proposed hardware accelerated algorithm's adaptations shows an achieved speedup of up to 7 times, as compared to a highly optimized CPU‐only based version.

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Human identity recognition in aerial images

Abstract: Abstract

Cited by 118 publications

References 20 publications

Person re-identification with mutual re-ranking

Person re-identification with mutual re-ranking

Pedestrian Recognition in Aerial Video Using Saliency and Multi-Features

Real‐time surveillance detection system for medium‐altitude long‐endurance unmanned aerial vehicles

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