Pedestrian Detection in Far Infrared Images based on the use of Probabilistic Templates

Bertozzi, Massimo; Broggi, Alberto; Gómez, Cristina Hilario; Fedriga, Rean Isabella; Vezzoni, G.; Rose, Michael Del

doi:10.1109/ivs.2007.4290135

Cited by 31 publications

(22 citation statements)

References 26 publications

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“…For ROI generation in IR images a natural solution would be to use a threshold, like in [12], or even better an adaptive threshold by assuming that non-pedestrian intensities follow a Gaussian distribution [15]. Unfortunately the problem of estimating an appropriate threshold remains a key issue because the pedestrian intensities vary with respect to range and outside temperature.…”

Section: Introductionmentioning

confidence: 99%

Intensity self similarity features for pedestrian detection in Far-Infrared images

Miron

Besbes

Rogozan

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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Pedestrian detection is an important but challenging component of an Intelligent Transportation System. In this paper, we describe a pedestrian detection system based on a monocular vision with a Far-Infrared camera (FIR). We propose an original feature representation, called Intensity Self Similarity (ISS) , adapted to pedestrian detection in FIR images. The ISS representation is based on the relative intensity self similarity within a pedestrian region of interest (ROI) hypothesis. Our system consists of two components. The first component generates pedestrian ROI hypothesis by exploiting the specific characteristics of FIR images, where pedestrian shapes may vary in large scale, but heads appear usually as light regions. Pedestrian ROI are detected, with high recall rate, due to a Hierarchical Codebook (HC) of Speeded-Up Robust Features (SURF) located in light head regions. The second component consists of pedestrian hypothesis validation, by using a pedestrian full-body classification based on the ISS representation, with Support Vector Machine (SVM). For classification, we retained two feature descriptors: the Histogram of Oriented Gradients (HOG) descriptor and the original ISS feature representation that we proposed for FIR images. The early fusion of these two features enhances significantly the system precision, attaining an F-measure for the pedestrian class of 97.7%. Moreover, this feature fusion outperforms the state-of-the-art SURF descriptor proposed previously. The experimental evaluation shows that our pedestrian detector is also robust, since it performs well in detecting pedestrians even in large scale and crowded real-world scenes.

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Section: Introductionmentioning

confidence: 99%

Intensity self similarity features for pedestrian detection in Far-Infrared images

Miron

Besbes

Rogozan

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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“…Classification is done by SURF feature matching that cast votes for object center locations in a 3D Hough voting space. Probabilistic models are employed by [29]. They use four different models in order to recognize the pose of the pedestrians: open, almost open, almost closed and fully closed legs are detected.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Brehar

Nedevschi

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined scales. We port this idea to the infrared domain. Our contributions reside in the combination of four channel features, namely infrared, histogram of gradient orientations, normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared sensors. Multiple scale feature computation is done by feature approximation. Another contribution is the study of different formulations for Local Binary Patterns like uniform patterns and rotation invariant patterns and their effect on detection performance. The detection speed is also boosted by the aid of a fast morphological based region of interest generator. We vary the number of approximated scales per octave and study the impact on execution time and accuracy. A reasonable result hits a speed of 18fps with a log average miss rate of 39%.

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“…A tracking algorithm is also implemented. [3] uses probabilistic template models of four different poses for detection. [30] also uses probabilistic template models, here they use three models representing different scales.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery

Gade

Jorgensen

Moeslund

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

102

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Pedestrian Detection in Far Infrared Images based on the use of Probabilistic Templates

Cited by 31 publications

References 26 publications

Intensity self similarity features for pedestrian detection in Far-Infrared images

Intensity self similarity features for pedestrian detection in Far-Infrared images

Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery

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