Robust real-time periodic motion detection, analysis, and applications

Cutler, Ross; Davis, Larry S.

doi:10.1109/34.868681

Cited by 578 publications

(411 citation statements)

References 29 publications

(23 reference statements)

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“…Some authors combine appearance and motion expanding their own previous works to more than one frame [7,16]; they improve significantly the results, but do not generate a motion model as an independent entity. Some approaches use only the motion information [5,15]. [5] applies time-frequency analysis to detect and characterize the human periodic motion and [15] detects patterns of human motion using optical flow and an SVM classifier.…”

Section: State Of the Artmentioning

confidence: 99%

“…Some approaches use only the motion information [5,15]. [5] applies time-frequency analysis to detect and characterize the human periodic motion and [15] detects patterns of human motion using optical flow and an SVM classifier.…”

Section: State Of the Artmentioning

confidence: 99%

“…The pattern of human motion is well known to be readily discriminative from other types of motions [5,7,16]. We introduce a new human motion representation that is mainly based on the use of the ISM framework [11] and the motion information in the MoSIFT descriptor [4].…”

Section: People Motion Modelmentioning

confidence: 99%

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People detection based on appearance and motion models

García-Martín

Hauptmann

Martinez

2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractThe main contribution of this paper is a new people detection algorithm based on motion information. The algorithm builds a people motion model based on the Implicit Shape Model (ISM) Framework and the MoSIFT descriptor. We also propose a detection system that integrates appearance, motion and tracking information. Experimental results over sequences extracted from the TRECVID dataset show that our new people motion detector produces results comparable to the state of the art and that the proposed multimodal fusion system improves the obtained results combining the three information sources.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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People detection based on appearance and motion models

García-Martín

Hauptmann

Martinez

2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…Distance between two legs is represented by periodic and quasi-periodic signals which aid to count people in a group [6], [23]. They show that these signals extracted from a single person and from occluded people are of different patterns.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Counting People in Crowded Areas by Using Local Empirical Templates and Density Ratios

Hung

Hsu

Chung

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a fast and automated method of counting pedestrians in crowded areas is proposed along with three contributions. We firstly propose Local Empirical Templates (LET), which are able to outline the foregrounds, typically made by single pedestrians in a scene. LET are extracted by clustering foregrounds of single pedestrians with similar features in silhouettes. This process is done automatically for unknown scenes. Secondly, comparing the size of group foreground made by a group of pedestrians to that of appropriate LET captured in the same image patch with the group foreground produces the density ratio. Because of the local scale normalization between sizes, the density ratio appears to have a bound closely related to the number of pedestrians who induce the group foreground. Finally, to extract the bounds of density ratios for groups of different number of pedestrians, we propose a 3D human models based simulation in which camera viewpoints and pedestrians' proximity are easily manipulated. We collect hundreds of typical occluded-people patterns with distinct degrees of human proximity and under a variety of camera viewpoints. Distributions of density ratios with respect to the number of pedestrians are built based on the computed density ratios of these patterns for extracting density ratio bounds. The simulation is performed in the offline learning phase to extract the bounds from the distributions, which are used to count pedestrians in online settings. We reveal that the bounds seem to be invariant to camera viewpoints and humans' proximity. The performance of our proposed method is evaluated with our collected videos and PETS 2009's datasets. For our collected videos with the resolution of 320x240, our method runs in real-time with good accuracy and frame rate of around 30 fps, and consumes a small amount of computing resources. For PETS 2009's datasets, our proposed method achieves competitive results with other methods tested on the same datasets [1], [2]. key words: local empirical templates, local density ratios, density ratio bounds, and people counting

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“…Invariance and discriminative power of the color invariants is experimentally investigated here as the dimensions of cooccurrence matrix and the derived features for finding correspondences of objects. William [7] suggests motion tracking by deriving velocity vectors from point-to-point correspondence relations. Relaxation and optical flow are very attractive methodologies to detect the trajectories of objects [8].…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Tracking for the Web Camera

Chin

Choo

2003

Lecture Notes in Computer Science

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Abstract. In this paper, color invariant co-occurrence features for moving vehicle tracking in a known environment is proposed. It extracts moving areas shaped on objects in Web video sequences captured by the Web camera and detects tracks of moving objects. Color invariant co-occurrence matrices are exploited to extract the plausible object blocks and the correspondences between adjacent video frames. The measures of class separability derived from the features of co-occurrence matrices are used to improve the performance of tracking. The experimental results are presented.

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Robust real-time periodic motion detection, analysis, and applications

Cited by 578 publications

References 29 publications

People detection based on appearance and motion models

People detection based on appearance and motion models

Real-Time Counting People in Crowded Areas by Using Local Empirical Templates and Density Ratios

Moving Vehicle Tracking for the Web Camera

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