Joint Probabilistic Data Association Revisited

Rezatofighi, S. Hamid; Milan, Anton; Zhang, Zhen; Shi, Qinfeng; Dick, Anthony; Reid, Ian

doi:10.1109/iccv.2015.349

Cited by 299 publications

(188 citation statements)

References 36 publications

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“…Various solutions exist for the same like Joint Probabilistic Data Association (JPDA) [10] by Fortmann et al, Probability Hypothesis Density (PHD) filter [11] by Mahler and Multi Hypothesis Tracking [12] by Reid. And there exist various improvements of these solutions like [13], [14] and [15]. But using these additional data association algorithms not only increases the complexity of the task but at times also can increase the execution time.…”

Section: A Overviewmentioning

confidence: 99%

Graph based vehicle infrastructure cooperative localization

Gulati

Zhang

Malovetz

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicleinfrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

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Section: A Overviewmentioning

confidence: 99%

Graph based vehicle infrastructure cooperative localization

Gulati

Zhang

Malovetz

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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“…In other words, they are well equipped to handle challenges numbers 1 and 2 listed in the first paragraph of Section 1. In the last two years, there has been a resurgence of detection-based approaches, including new variations of the more mature multi-target tracking algorithms, for example a revisiting of the JPDAF [33]. Most recently, due to the vast amount of research on (deep) neural networks for object and pedestrian detection [23,[34][35][36][37][37][38][39][40][41], track-by-detection multi-target tracking approaches are starting to be proposed that make use of these stat-of-the-art detectors [42].…”

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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“…However, this method is difficult to deal with numerous targets and measurements such as multiple human objects tracking in crowded scenes. To overcome this drawback, Rezatofighi et al [21] revisit the JPDAF technique and propose a novel solution in formulating the problem as an integer linear program, which is embedded in a simple tracking framework. More specifically, the proposed method reformulates the calculation of individual JPDA assignment scores as a series of integer linear programs, and approximates the joint score by the m-best solutions, which is efficiently calculated by using a binary tree partition method, and hence addresses the issue of high computational complexity associated with JPDAF without forfeiting tracking performance.…”

Section: Jpdafmentioning

confidence: 99%

“…The most widely used target association techniques include joint probability data association filtering (JPDAF) [18][19][20][21], multiple-hypothesis tracking (MHT) [22][23][24][25], and flow network framework (FNF) [26][27][28][29]. The JPDAF computes a Bayesian estimate of the correspondence between two consecutive frames, based on calculating all possible target-measurement association probabilities jointly.…”

Section: Human Tracking Within a Cameramentioning

confidence: 99%

Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across nonoverlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

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Joint Probabilistic Data Association Revisited

Cited by 299 publications

References 36 publications

Graph based vehicle infrastructure cooperative localization

Graph based vehicle infrastructure cooperative localization

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

Human tracking over camera networks: a review

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