Human detection using partial least squares analysis

Schwartz, William Robson; Kembhavi, Aniruddha; Harwood, David; Davis, Larry S.

doi:10.1109/iccv.2009.5459205

Cited by 411 publications

(241 citation statements)

References 21 publications

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“…To do so, we first train a person detector based on a PLS appearance model that works with two classes (positive and negative person samples) [19]. The training is achieved by cropping the samples into overlapping blocks and extracting low-level features from each of them.…”

Section: Person Re-identification Based On Partial Least Squaresmentioning

confidence: 99%

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Person Re-identification Using Partial Least Squares Appearance Modeling

Prado

Schwartz

Pedrini

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Due to the large areas covered by surveillance systems, employed cameras usually lack intersection of field of view, refraining us from mapping the location of a person in a camera to another one. Therefore, when a subject appears in a camera, a person re-identification method is required to discover whether the subject has been previously identified in a different camera. Even though several approaches have been proposed in the literature, person re-identification is still a challenging problem due to appearance variation between cameras, changes in illumination, pose variation, and low quality data, among others. To reduce the effect of the aforementioned difficulties, we propose a person re-identification approach that models the appearance of the subjects based on multiple samples collected from multiple cameras and employs person detection and tracking to enhance the robustness of the method. Experiments conducted on three public available data sets demonstrate improvements over existing methods.

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Section: Person Re-identification Based On Partial Least Squaresmentioning

confidence: 99%

“…A nonmaximum suppression is applied in the results to clean up redundant detection windows in multiple scales. A more detailed description can be found in [19]. The next step is to group detection windows from sequential frames of the same camera into tracklets.…”

Section: Person Re-identification Based On Partial Least Squaresmentioning

confidence: 99%

Person Re-identification Using Partial Least Squares Appearance Modeling

Prado

Schwartz

Pedrini

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…SVMs have been used with other descriptors for whole bodies [16] or body parts [19]. Schwartz et al [25] further incorporated texture information.…”

Section: Previous Workmentioning

confidence: 99%

People Detection Using Color and Depth Images

Salas

Tomasi

2011

Lecture Notes in Computer Science

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Abstract. We present a strategy that combines color and depth images to detect people in indoor environments. Similarity of image appearance and closeness in 3D position over time yield weights on the edges of a directed graph that we partition greedily into tracklets, sequences of chronologically ordered observations with high edge weights. Each tracklet is assigned the highest score that a Histograms-of-Oriented Gradients (HOG) person detector yields for observations in the tracklet. High-score tracklets are deemed to correspond to people. Our experiments show a significant improvement in both precision and recall when compared to the HOG detector alone.

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“…or/and modalities (intensity, depth, motion, etc.) into a single pattern classification module [8], [33], [37], [38], [43], [46], [48]. One fusion approach involves integration of all cues into a single joint feature space [38], [43], [46].…”

Section: Previous Workmentioning

confidence: 99%

The Benefits of Dense Stereo for Pedestrian Detection

Keller

Enzweiler

Rohrbach

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper presents a novel pedestrian detection system for intelligent vehicles. We propose the use of dense stereo for both the generation of regions of interest and pedestrian classification. Dense stereo allows the dynamic estimation of camera parameters and the road profile, which, in turn, provides strong scene constraints on possible pedestrian locations. For classification, we extract spatial features (gradient orientation histograms) directly from dense depth and intensity images. Both modalities are represented in terms of individual feature spaces, in which discriminative classifiers (linear support vector machines) are learned. We refrain from the construction of a joint feature space but instead employ a fusion of depth and intensity on the classifier level. Our experiments involve challenging image data captured in complex urban environments (i.e., undulating roads and speed bumps). Our results show a performance improvement by up to a factor of 7.5 at the classification level and up to a factor of 5 at the tracking level (reduction in false alarms at constant detection rates) over a system with static scene constraints and intensity-only classification.

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Human detection using partial least squares analysis

Abstract: Significant research has been devoted to detecting people in images and videos. In this paper we describe a human detection

Cited by 411 publications

References 21 publications

Person Re-identification Using Partial Least Squares Appearance Modeling

Person Re-identification Using Partial Least Squares Appearance Modeling

People Detection Using Color and Depth Images

The Benefits of Dense Stereo for Pedestrian Detection

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