An Online Learned Elementary Grouping Model for Multi-target Tracking

Chen, Xiaojing; Qin, Zhen; An, Le; Bhanu, Bir

doi:10.1109/cvpr.2014.162

Cited by 40 publications

(53 citation statements)

References 26 publications

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“…Chen et al learn elementary groups to infer high level context information to improve the tracking of multiple people [12]. They detect pairwise grouping based on social behavior, which is later used to create a general grouping graph used for the tracking.…”

Section: Socially-aware Behavior Analysismentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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Tag is a popular children's playground game. It revolves around taggers that chase and then tag runners, upon which their roles switch. There are many variations of the game that aim to keep children engaged by presenting them with challenges and different types of gameplay. We argue that the introduction of sensing and floor projection technology in the playground can aid in providing both variation and challenge. To this end, we need to understand players' behavior in the playground and steer the interactions using projections accordingly. In this paper, we first analyze the behavior of taggers and runners in a traditional tag setting. We focus on behavioral cues that differ between the two roles. Based on these, we present a probabilistic role recognition model. We then move to an interactive setting and evaluate the model on tag sessions in an interactive tag playground. Our model achieves 77.96 % accuracy, which demonstrates the feasibility of our approach. We identify several avenues for improvement. Eventually, these should lead to a more thorough understanding of what happens in the playground, not only regarding player roles but also when the play breaks down, for example when players are bored or cheat.

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Section: Socially-aware Behavior Analysismentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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“…In order not to limit ourselves to the high confidence locations found by c a + c m + c nm , we further sample an extra 10 candidates in a small neighborhood, (6 × 6) of each extrema point (Figure 7(d)). The latter step will allow the quadratic terms to make the necessary changes to the target locations in TABLE 1 Quantitative results of our method in terms of Tracking Accuracy when pixel threshold is set to 15. We compared our method with six competitors on nine sequences of [11].…”

Section: Speed-upmentioning

confidence: 99%

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Dehghan

Shah

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Multi-object tracking has been studied for decades. However, when it comes to tracking pedestrians in extremely crowded scenes, we are limited to only few works. This is an important problem which gives rise to several challenges. Pre-trained object detectors fail to localize targets in crowded sequences. This consequently limits the use of data-association based multi-target tracking methods which rely on the outcome of an object detector. Additionally, the small apparent target size makes it challenging to extract features to discriminate targets from their surroundings. Finally, the large number of targets greatly increases computational complexity which in turn makes it hard to extend existing multi-target tracking approaches to high-density crowd scenarios. In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently. We formulate online crowd tracking as Binary Quadratic Programing. Our formulation employs target's individual information in the form of appearance and motion as well as contextual cues in the form of neighborhood motion, spatial proximity and grouping constraints, and solves detection and data association simultaneously. In order to solve the proposed quadratic optimization efficiently, where state-of art commercial quadratic programing solvers fail to find the answer in a reasonable amount of time, we propose to use the most recent version of the Modified Frank Wolfe algorithm, which takes advantage of SWAP-steps to speed up the optimization. We show that the proposed formulation can track hundreds of targets efficiently and improves state-of-art results by significant margins on eleven challenging high density crowd sequences.

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“…Historically it is one of the major challenges in multi-target tracking [4] and is still an active research topic [43][44][45]. It is desirable for targets to remain separated within the image and for no occlusions to take place; however, this is often not the case.…”

Section: Common Multi-target Tracking Algorithmsmentioning

confidence: 99%

Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

Hoak¹,

Medeiros²,

Povinelli³

2017

Preprint

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Abstract:We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

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An Online Learned Elementary Grouping Model for Multi-target Tracking

Cited by 40 publications

References 26 publications

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

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