Pedestrian Recognition Based on Saliency Detection and Kalman Filter Algorithm in Aerial Video

Wang, Xingbao; Liu, Chunping; Gong, Li; Liu, Long; Gong, Shengrong

doi:10.1109/cis.2011.263

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Recently, Xingbao et al [30] used saliency detection to segment pedestrians from an aerial video. The goal of this paper is to incorporate saliency detection into the face detection framework, especially for FRAD applications, and inform the FRAD community of the advantages of saliency detection.…”

Section: Saliency Detectionmentioning

confidence: 99%

Face detection at a distance using saliency maps

El-Barkouky

Rara

Farag

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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Face detection has been one of the most studied topics in the biometrics literature. A critical factor in the face detection process is the complexity of the setup used to acquire the images. The face detection might be considered a solved problem in easy setups but for outdoor setups with unconstrained illumination and background conditions the problem is still hard. In this paper we propose using saliency maps as a clue for false positive rejection in the applications that require face detection at a distance in complex unconstrained outdoor backgrounds. The saliency maps will be used in a robust score fusion approach combined with other aspects like facial features and skin color. The advantage in using saliency maps is that it tackles the problem from another perspective that is not affected by pose, illumination and expression problems. Experimental results show that the proposed method is promising.

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Section: Saliency Detectionmentioning

confidence: 99%

Face detection at a distance using saliency maps

El-Barkouky

Rara

Farag

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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“…Recently, Xingbao et al [34] used saliency detection to segment pedestrians from an aerial video. In this work, it is used to help in rejecting false positives through the RSFFD framework.…”

Section: Saliency Detectionmentioning

confidence: 99%

Mathematical modeling for partial object detection.

El-Barkouky¹

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From a computer vision point of view, the image is a scene consisting of objects of interest and a background represented by everything else in the image. The relations and interactions among these objects are the key factors for scene understanding. In this dissertation, a mathematical model is designed for the detection of partially occluded faces captured in unconstrained real life conditions. The proposed model novelty comes from explicitly considering certain objects that are common to occlude faces and embedding them in the face model. This enables the detection of faces in difficult settings and provides more information to subsequent analysis in addition to the bounding box of the face.In the proposed Selective Part Models (SPM), the face is modelled as a collection of parts that can be selected from the visible regular facial parts and some of the occluding objects which commonly interact with faces such as sunglasses, caps, hands, shoulders, and other faces. With the face detection being the first step in the face recognition pipeline, the proposed model does not only detect partially occluded faces efficiently but it also suggests the occluded parts to be excluded from the subsequent recognition step. The model was tested on several recent face detection databases and benchmarks and achieved state of the art performance. In addition, detailed analysis for the performance with respect vi to different types of occlusion were provided. Moreover, a new database was collected for evaluating face detectors focusing on the partial occlusion problem.This dissertation highlights the importance of explicitly handling the partial occlusion problem in face detection and shows its efficiency in enhancing both the face detection performance and the subsequent recognition performance of partially occluded faces. The broader impact of the proposed detector exceeds the common security applications by using it for human robot interaction. The humanoid robot Nao is used to help in teaching children with autism and the proposed detector is used to achieve natural interaction between the robot and the children by detecting their faces which can be used for recognition or more interestingly for adaptive interaction by analyzing their expressions. vii

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Pedestrian Recognition Based on Saliency Detection and Kalman Filter Algorithm in Aerial Video

Cited by 2 publications

References 10 publications

Face detection at a distance using saliency maps

Face detection at a distance using saliency maps

Mathematical modeling for partial object detection.

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