PETS2009 and Winter-PETS 2009 results: A combined evaluation

Ellis, Anna-Louise; Shahrokni, Ali; Ferryman, James

doi:10.1109/pets-winter.2009.5399728

Cited by 58 publications

(53 citation statements)

References 8 publications

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“…And, yet, it has been shown to outperform many state-of-theart methods on the PETS'09 data set [45]. Its main limitation is that, because it does not exploit appearance, it cannot prevent identity switches when people come close to each other.…”

Section: Multiple Target Trackingmentioning

confidence: 99%

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Multi-Commodity Network Flow for Tracking Multiple People

Shitrit

Berclaz

Fleuret

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

153

160

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Abstract-In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.

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Section: Multiple Target Trackingmentioning

confidence: 99%

“…We use the publicly available PETS'09 3 dataset, for which the performance of other algorithms has been published [45]. More specifically, we tested our method on the 800-frame sequence S2/L1, which is filmed by 7 cameras at 7 fps, and features 10 people.…”

Section: Pedestrians -Pets'09mentioning

confidence: 99%

Multi-Commodity Network Flow for Tracking Multiple People

Shitrit

Berclaz

Fleuret

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

153

160

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show abstract

“…In our e xperiments, we used four camera v iews (view 1,5,6,8) and co mpared our detection results with POM [10], wh ich is one of the top-performers in W inter-PETS2009 [15] (the evaluation results of POM on the PETS09 S2L1 dataset come fro m [6]). We also co mpare our method with the method in [6], one of the latest results on this dataset.…”

Section: Experiments Resultsmentioning

confidence: 99%

Multi-camera Pedestrian Detection with Multi-view Bayesian Network Model

Peng¹,

Wang²,

Huang³

2012

Procedings of the British Machine Vision Conference 2012

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In this paper, we propose a novel method with the multi-view Bayesian network (M BN) model to detect pedestrians from multi-camera surveillance videos. In our method, the ground plane is discretized in a predefined set of locations and our aim is to estimate the occupancy probability of each location that can be then used to predict the occurrence of pedestrians. To reduce the possible phantoms, we use M BN to model the potential occlusion relationship of all locations in all views, and the "subjective supposing" node states (SSNS) as a set of Boolean parameters of M BN to denote whether a pedestrian occurs at the corresponding location. Thus a learning algorithm is proposed to estimate the SSNS parameters, by finding such a configuration that the final occupancy possibility can best explain the image observations (i.e., foreground masks) from different views. The experimental results on the APIDIS and PETS09 S2L1 benchmark datasets show that our method can obtain at least 10% performance gain compared with several state-of-the-art algorithms.

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“…We evaluate our algorithm on the PETS2009 dataset [26], a challenging benchmark dataset for multiview crowd image analysis containing outdoor sequences with varying crowd densities and activities. We tested on two tasks: crowd detection in a sparse crowd (sequence S2L1-1234) and crowd counting in a dense crowd (sequence S1L1-1357).…”

Section: Methodsmentioning

confidence: 99%

“…We compared our detection results against the ASEF method, which is a detection method using convolution of learned average of synthetic exact filters [5], and the POM+LP method, which is a multi-target detection and tracking algorithm based on a probabilistic occupancy map and linear programming [24]. We chose these two methods because they are the current top-performers as reported in Winter-PETS2009 [26]. We also compared against the Cascade [8] and Part-based [9] person detectors, trained according to [5].…”

Section: Methodsmentioning

confidence: 99%

Crowd Detection with a Multiview Sampler

Collins

2010

Computer Vision – ECCV 2010

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Abstract. We present a Bayesian approach for simultaneously estimating the number of people in a crowd and their spatial locations by sampling from a posterior distribution over crowd configurations. Although this framework can be naturally extended from single to multiview detection, we show that the naive extension leads to an inefficient sampler that is easily trapped in local modes. We therefore develop a set of novel proposals that leverage multiview geometry to propose global moves that jump more efficiently between modes of the posterior distribution. We also develop a statistical model of crowd configurations that can handle dependencies among people and while not requiring discretization of their spatial locations. We quantitatively evaluate our algorithm on a publicly available benchmark dataset with different crowd densities and environmental conditions, and show that our approach outperforms other state-of-the-art methods for detecting and counting people in crowds.

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PETS2009 and Winter-PETS 2009 results: A combined evaluation

Cited by 58 publications

References 8 publications

Multi-Commodity Network Flow for Tracking Multiple People

Multi-Commodity Network Flow for Tracking Multiple People

Multi-camera Pedestrian Detection with Multi-view Bayesian Network Model

Crowd Detection with a Multiview Sampler

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