Pedestrian Detection via Periodic Motion Analysis

Yang, Ruigang; Weiss, Isaac; Zheng, Qinfen; Davis, Larry S.

doi:10.1007/s11263-006-8575-4

Cited by 64 publications

(48 citation statements)

References 29 publications

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“…Different from this class of techniques, a large learning set is unavailable for our applications such as detecting rare birds. Periodic motion detection (Ran et al 2007;Briassouli and Ahuja 2007) assumes objects, such as animals, with periodic motion pattern and applies time-frequency analysis (Briassouli and Ahuja 2007) or image sequence alignment (Laptev et al 2005) to capture the periodicity. Applications of periodic motion detection have been found to vehicles, humans, and even canines.…”

Section: Related Workmentioning

confidence: 99%

A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method

Song

2010

IEEE Trans. on Image Process.

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We report a new filter for assisting the search for rare bird species. Since a rare bird only appears in front of the camera with very low occurrence (e.g. less than ten times per year) for very short duration (e.g. less than a fraction of a second), our algorithm must have very low false negative rate. We verify the bird body axis information with the known bird flying dynamics from the short video segment. Since a regular extended Kalman filter (EKF) cannot converge due to high measurement error and limited data, we develop a novel Probable Observation Data Set (PODS)-based EKF method. The new PODS-EKF searches the measurement error range for all probable observation data that ensures the convergence of the corresponding EKF in short time frame.The algorithm has been extensively tested in experiments. The results show that the algorithm achieves 95.0% area under ROC curve in physical experiment with close to zero false negative rate.

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Section: Related Workmentioning

confidence: 99%

A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method

Song

2010

IEEE Trans. on Image Process.

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“…Proposed Approach Periodicity is a significant feature of objects in motion, and it has been widely used for segmenting objects [1] and detecting pedestrians [5]. The regions in input image frames of an IM train video can be divided into two types: Region A is the region above and below the containers (see Fig.…”

Section: Conventional Foreground Segmentation Techniquesmentioning

confidence: 99%

Robust segmentation of freight containers in train monitoring videos

Kong

Kumar

Ahuja

et al. 2009

2009 Workshop on Applications of Computer Vision (WACV)

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This paper is about a vision-based system that automatically monitors intermodal freight trains for the quality of how the loads (containers) are placed along the train. An accurate and robust algorithm to segment the foreground of containers in videos of the moving train is indispensable for this purpose. Given a video of a moving train consisting of containers of different types, this paper presents a method exploiting the information in both frequency and spatial domains to segment these containers. This method can accurately segment all types of containers under a variety of background conditions, e.g illumination variations and moving clouds, in the train videos shot by a fixed camera. The accuracy and robustness of the proposed method are substantiated through a large number of experiments on real data of train videos.

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“…The well-cited method of Cutler and Davis [10] calculates the correlation of successive frames, resulting in a similarity plot over time. Ran et al [26] improved the results of [10] by applying hypothesis testing (based on periodogram maximizer) on individual pixels, to check whether a given pixel is periodic. Only the periodic pixels are then used to estimate the dominant period in the sequence.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach is appearance-based compatible to [6,26] discussed above. Just as most of the approaches, tracking is performed first to align the object in the center of the frame.…”

Section: Periodicity Analysismentioning

confidence: 99%

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Visual quasi-periodicity

Pogalin¹,

Smeulders

Thean³

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

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Periodicity is at the core of the recognition of many actions. This paper takes the following steps to detect and measure periodicity. 1) We establish a conceptual framework of classifying periodicity in 10 essential cases, the most important of which are flashing (of a traffic light), pulsing (of an anemone), swinging (of wings), spinning (of a swimmer), turning (of a conductor), shuttling (of a brush), drifting (of an escalator) and thrusting (of a kangaroo). 2) We present an algorithm to detect all cases by the one and the same algorithm. It tracks the object independent of the object's appearance, then performs probabilistic PCA and spectral analysis followed by detection and frequency measurement. The method shows good performance with fixed parameters for examples of all above cases assembled from the Internet. 3) Application of the method, completely unaltered, to a random half hour of CNN news has led to an 80% score.

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Pedestrian Detection via Periodic Motion Analysis

Cited by 64 publications

References 29 publications

A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method

A Low False Negative Filter for Detecting Rare Bird Species From Short Video Segments Using a Probable Observation Data Set-Based EKF Method

Robust segmentation of freight containers in train monitoring videos

Visual quasi-periodicity

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