Pedestrian detection using 3D optical flow sequences for a mobile robot

Nakada, Takahiro; Kagami, Satoshi; Mizoguchi, Hiroshi

doi:10.1109/icsens.2008.4716556

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Table I compares the speed and accuracy of several fast vision-based detection methods, including C 4 , the method proposed in this paper. 1 There have been numerous previous works in the robotics community which developed pedestrian detection systems for mobile robot platforms [18], [19], [20], [21]. The majority of these works employ some form of ranging sensor (representative examples are [18] and [21]).…”

Section: Related Workmentioning

confidence: 99%

Real-time human detection using contour cues

Geyer

Rehg

2011

2011 IEEE International Conference on Robotics and Automation

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A real-time and accurate human detector, C 4 , is proposed in this paper. C 4 achieves 20 fps speed and stateof-the-art detection accuracy, using only one processing thread without resorting to special hardwares like GPU. Real-time accurate human detection is made possible by two contributions. First, we show that contour is exactly what we should capture and signs of comparisons among neighboring pixels are the key information to capture contours. Second, we show that the CENTRIST visual descriptor is particularly suitable for human detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image pre-processing or feature vector normalization, and only requires O(1) steps to test an image patch. C 4 is also friendly to further hardware acceleration. In a robot with embedded 1.2GHz CPU, we also achieved accurate and 20 fps high speed human detection.

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Section: Related Workmentioning

confidence: 99%

Real-time human detection using contour cues

Geyer

Rehg

2011

2011 IEEE International Conference on Robotics and Automation

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“…Optical flow is an accurate technique to capture and estimate a human object in a frame sequence; however, it requires the object to be in motion. An application on a mobile robot is presented in [29]. A mixture model representation of salient patterns of optical flow is proposed in [35] to explain motion patterns.…”

Section: Introductionmentioning

confidence: 99%

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Goodman

et al. 2013

ICA

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A Support Vector Machine (SVM) is an effective method for pedestrian detection applications; however, performance of an SVM is closely related to the samples that are used to train it. An SVM classifier trained by samples from well-known pedestrian datasets such as INRIA and MIT is observed to have limited detection capability in practical environments. In this paper, a statistical background-foreground extraction approach is proposed that autonomously generates samples containing pedestrians in real scenes, in order to diversify the basic training set of the SVM. Comparative experiments have shown that the SVM classifier's discriminability and adaptability to a new scene are greatly enhanced by utilizing extracted samples from that scene in the training stage. Here, a pedestrian tracker that combines a Camshift tracker and a Kalman filter is adjoined to the pedestrian classifier; the tracker is proved to be robust against pose and scale changes, abrupt direction of motion changes, and occlusions, in several test scenes.

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“…T HE ability to detect pedestrians in images has a major impact on applications such as video surveillance [2], smart vehicles [3], [4], robotics [5]. Changing variations in human body poses and clothing, combined with varying cluttered backgrounds and environmental conditions, make this problem far from being solved.…”

Section: Introductionmentioning

confidence: 99%

Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns

Zheng¹,

Shen²,

Hartley³

et al. 2010

Preprint

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Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary patterns (CS-LBP) and pyramid center-symmetric local binary/ternary patterns (CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. [1] mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information and some texture information. Moreover, the proposed dense CS-LBP and the pyramid CS-LBP/LTP are easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the dense CS-LBP feature with linear supporct vector machines (SVMs) is comparable with the histograms of oriented gradients (HOG) feature with linear SVMs, and the pyramid CS-LBP/LTP features outperform both HOG features with linear SVMs and the start-of-the-art pyramid HOG (PHOG) feature with the histogram intersection kernel SVMs. We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance-producing state-of-the-art accuracy on the INRIA pedestrian dataset.

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Pedestrian detection using 3D optical flow sequences for a mobile robot

Cited by 7 publications

References 7 publications

Real-time human detection using contour cues

Real-time human detection using contour cues

Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns

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