Real-time pedestrian detection with deformable part models

Cho, Hyunggi; Rybski, Paul E.; Bar-Hillel, Aharon; Zhang, Wende

doi:10.1109/ivs.2012.6232264

Cited by 61 publications

(28 citation statements)

References 22 publications

(54 reference statements)

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“…In his paper, Yan et al [32] presented an extension of the prevalent deformable part model [33] (DPM) called Multi task DPM (MT-DPM) which aims to explore the relation among multiple resolutions by combining an optimal DPM detector and resolution aware transformations. It takes the pedestrians from multiple resolutions and determines their commonness and differences jointly.…”

Section: ) Deformable Part Based Detectionmentioning

confidence: 99%

Intelligent Pedestrian Detection using Optical Flow and HOG

Ramzan¹,

Fatima²,

Raza³

et al. 2016

ijacsa

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Abstract-Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians' motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.

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Section: ) Deformable Part Based Detectionmentioning

confidence: 99%

Intelligent Pedestrian Detection using Optical Flow and HOG

Ramzan¹,

Fatima²,

Raza³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Several works exist which apply the aforementioned algorithms on traffic safety applications, and are thus related to our work. However, to the best of our knowledge, often only forward-looking cameras are used and only single object classes (pedestrians or bicyclists) are detected [3,4,16]. In [15] we presented a similar safety system, however only targeting pedestrians which evidently is a much easier scenario.…”

Section: Related Workmentioning

confidence: 99%

Efficient multiclass object detection: Detecting pedestrians and bicyclists in a truck's blind spot camera

Beeck

Goedemé

2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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In this paper we propose an efficient detection and tracking framework targeting vulnerable road users in the blind spot camera images of a truck. Existing non-vision based safety solutions are not able to handle this problem completely. Therefore we aim to develop an active safety system, based solely on the vision input of the blind spot camera. This is far from trivial: vulnerable road users are a diverse class and consist of a wide variety of poses and appearances. Evidently we need to achieve excellent accuracy results and furthermore we need to cope with the large lens distortion and extreme viewpoints induced by the blind spot camera. In this work we present a multiclass detection methodology which enables the efficient detection of both pedestrians and bicyclists in these challenging images. To achieve this we propose the integration of a warping window approach with multiple object detectors which we intelligently combine in a probabilistic manner. To validate our framework we recorded several simulated dangerous blind spot scenarios with a genuine blind spot camera mounted on a real truck. We show that our approach achieves excellent accuracy on these challenging datasets.

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“…The idea of the Histogram of Oriented Gradients (HOG) descriptor by using then slidingwindow detection formulation was proposed by Dalal and Triggs [1]. It has become the most popular descriptor and many algorithms were proposed based on it [2,3,4].…”

Section: Related Workmentioning

confidence: 99%

A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles

Han

Tai

et al. 2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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In this work, we present a real-time pedestrian detection system using LIDAR and Vision in-vehicle. We get regions of interest by clustering lidar point clouds and project them onto the images. After that we use black mask to replace those image areas which has no lidar points projected onto. Then we extract HOG and lidar point clouds features and use those features to detect pedestrians by a linear SVM classifier. The main contributions are that we proposed a method that can select ROIs on image automatically and then enhanced the HOG descriptor with the lidar points' projections. Finally we fuse HOG and lidar based features to train a linear SVM to detect pedestrian. The above method we proposed can satisfy real-time requirement. We apply our pedestrian detection system to our own dataset and KITTI dataset, and show that we outperform the primitive HOG based methods.

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Real-time pedestrian detection with deformable part models

Cited by 61 publications

References 22 publications

Intelligent Pedestrian Detection using Optical Flow and HOG

Intelligent Pedestrian Detection using Optical Flow and HOG

Efficient multiclass object detection: Detecting pedestrians and bicyclists in a truck's blind spot camera

A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles

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