Edge based segmentation for pedestrian detection using NIR camera

Kancharla, Tarun; Kharade, Pallavi; Gindi, Sanjyot; Kutty, Krishnan; Vaidya, Vinay G.

doi:10.1109/iciip.2011.6108965

Cited by 25 publications

(16 citation statements)

References 9 publications

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“…In this method, human characteristics including the ratio of height to width [3,6], and the location of the hottest spot (head) [7,8] in the ROI are extracted and used in a heuristically designed decision rule. To be classified as containing a human, the ratio of height to width must fall within the range of 0.8 to 1.8.…”

Section: Shape-dependent Methodsmentioning

confidence: 99%

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Human Detection Based on Infrared Images in Forestry Environments

Ostovar

Hellström

Ringdahl

2016

Lecture Notes in Computer Science

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Abstract. It is essential to have a reliable system to detect humans in close range of forestry machines to stop cutting or carrying operations to prohibit any harm to humans. Due to the lighting conditions and high occlusion from the vegetation, human detection using RGB cameras is difficult. This paper introduces two human detection methods in forestry environments using a thermal camera; one shape-dependent and one shape-independent approach. Our segmentation algorithm estimates location of the human by extracting vertical and horizontal borders of regions of interest (ROIs). Based on segmentation results, features such as ratio of height to width and location of the hottest spot are extracted for the shape-dependent method. For the shape-independent method all extracted ROI are resized to the same size, then the pixel values (temperatures) are used as a set of features. The features from both methods are fed into different classifiers and the results are evaluated using side-accuracy and side-efficiency. The results show that by using shapeindependent features, based on three consecutive frames, we reach a precision rate of 80% and recall of 76%.

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Section: Shape-dependent Methodsmentioning

confidence: 99%

“…We have developed and evaluated one shape-dependent and one shape-independent method. Shape-dependent methods are based on human characteristics [3,6] such as shape, height, length and location of the head [7,8]. Yasuno et al [7] used the P-tile method for head detection.…”

Section: Introductionmentioning

confidence: 99%

Human Detection Based on Infrared Images in Forestry Environments

Ostovar

Hellström

Ringdahl

2016

Lecture Notes in Computer Science

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“…In this system, a treestructured two-stage detector composed of Haar-like and HOG features is employed to verify the candidates. Kancharla et al [16] presented a fast and robust segmentation scheme to form potential candidate pedestrian image blocks.…”

Section: Related Workmentioning

confidence: 99%

Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

Lee

Chan

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Pedestrian detection is an important issue in the field of intelligent transportation systems. As a pedestrian is not an apparent object at nighttime, it brings about critical difficulties in effectively detecting a pedestrian for a driving assistant vision system. While using an infrared projector to enhance the illumination contrast, objects in a nighttime environment might reflect the infrared projected by the emitted spotlight. In some cases, however, the clothes on a pedestrian might absorb most of the infrared, thus causing the pedestrian to be partially invisible. To deal with this problem, a nighttime part-based pedestrian detection method is proposed. It divides a pedestrian into parts for a moving vehicle with a camera and a near-infrared lighting projector. Due to a high computation load, selecting effective parts becomes imperative. By analyzing the spatial relationship between every pair of parts, the confidence of the detected parts can be enhanced even when some parts are occluded. At the last stage of this system, the pedestrian detection result is refined by a block-based segmentation method. The system is verified by experiments, and the appealing results are demonstrated.Index Terms-Geometric information, histogram of oriented gradient (HOG), near infrared (NIR), nighttime, part based, pedestrian detection, spatial relationship.

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“…[11] . 스테레오 카메라에서 대칭성을 이용한 연구로 고도맵(elevation map)으로부터 추출된 보행자, 자동차, 도로 폴대와 같은 오브젝트의 윈도우를 구하고, 밝기 값과 이진 에지를 기준으로 두 개의 수직 대칭 맵 에서 구한 히스토그램들의 가중 합을 기준으로 보행자 검출 후보를 한정하는 방법이 제안 [15] 되었다.…”

Section: 반면에 Chnftrs 와 Hog 같이 추출이 복잡한 특징들은 인식률은 높으나 속도가 느린 단점이 있다unclassified

An Efficient Pedestrian Recognition Method based on PCA Reconstruction and HOG Feature Descriptor

Kim¹,

Baek²,

Kim³

2013

Journal of the Institute of Electronics and Information Enginee

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In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

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Edge based segmentation for pedestrian detection using NIR camera

Cited by 25 publications

References 9 publications

Human Detection Based on Infrared Images in Forestry Environments

Human Detection Based on Infrared Images in Forestry Environments

Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

An Efficient Pedestrian Recognition Method based on PCA Reconstruction and HOG Feature Descriptor

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