Occlusion Handling in Tracking Multiple People Using RNN

Babaee, Mohammadreza; Li, Zimu; Rigoll, Gerhard

doi:10.1109/icip.2018.8451140

Cited by 27 publications

(15 citation statements)

References 19 publications

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“…Another algorithm that relied solely on motion features was the one proposed in [132]. Babaee et al presented a LSTM which learned to predict the new position and size of the bounding box for every object in the scene, using information about position and velocity in previous frames.…”

Section: Other Approachesmentioning

confidence: 99%

Deep learning in video multi-object tracking: A survey

et al. 2020

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The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions.In this section, a general description about the problem of MOT is provided. The main characteristics and common steps of MOT algorithms are identified and described in section 2.1. The metrics that are usually employed to evaluate the performance of the models are discussed in section 2.2, while the most important benchmark datasets are presented in section 2.3. 7 The website says the detections were obtained using a model based on a latent SVM, or L-SVM. That model is now known as Deformable Parts Model (DPM). 8

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Section: Other Approachesmentioning

confidence: 99%

Deep learning in video multi-object tracking: A survey

et al. 2020

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“…The authors of [15] proposed to combine multiple cues such as appearance, motion and interaction cues within an RNN architecture. In [9], RNNs were used to predict the targets motion and tracklet-stitching was used to handle occlusions. All these methods used RNNs for modeling nonlinear temporal dependencies.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, more and more powerful detection algorithms brought a significant increase of performance [6], [7], [8]. Including motion information also improves the tracking performance [9], [10], [11]. Among the tracking approaches based on motion models, the linear dynamical model (i.e., constant object velocity) is the most commonly used [4], [10], [12].…”

Section: Introductionmentioning

confidence: 99%

“…While a linear dynamical model is suitable in case of high sampling rate and reasonable object velocity, challenging tracking scenarios with low sampling rate, moving camera, high object velocity, rapid changes of direction, and occlusions are still difficult to tackle with such simple models, thus leading to numerous tracking errors such as false negatives and identity switches. Therefore, more sophisticated nonlinear models using recurrent neural networks (RNNs) to capture the motion information have been proposed [9], [15], [16], [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder

Lin,

Girin,

Alameda-Pineda

2022

Preprint

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In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT. The DVAE is a latent-variable deep generative model that can be seen as an extension of the variational autoencoder for the modeling of temporal sequences. It is included in DVAE-UMOT to model the objects' dynamics, after being pre-trained on an unlabeled synthetic dataset of single-object trajectories. Then the distributions and parameters of DVAE-UMOT are estimated on each multi-object sequence to track using the principles of variational inference: Definition of an approximate posterior distribution of the latent variables and maximization of the corresponding evidence lower bound of the data likehood function. DVAE-UMOT is shown experimentally to compete well with and even surpass the performance of two state-of-the-art probabilistic MOT models. Code and data are publicly available.

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“…Recurrent neural networks (RNNs) have been proposed to model non-linear motion and measurement noise, 24 and to overcome the long-term occulsion problem. 25 Approaches that use a pure visual approach or the IOU score of detections work well on a stationary camera where the next frame points to the same position, but becomes unreliable when utilized in a scenario with a moving camera.…”

Section: Related Workmentioning

confidence: 99%

Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications

Xie,

Ide,

Izadi

et al. 2021

Preprint

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Multi-object tracking (MOT) is a crucial component of situational awareness in military defense applications. With the growing use of unmanned aerial systems (UASs), MOT methods for aerial surveillance is in high demand. Application of MOT in UAS presents specific challenges such as moving sensor, changing zoom levels, dynamic background, illumination changes, obscurations and small objects. In this work, we present a robust object tracking architecture aimed to accommodate for the noise in real-time situations. We propose a kinematic prediction model, called Deep Extended Kalman Filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space. DeepEKF utilizes a learned image embedding along with an attention mechanism trained to weight the importance of areas in an image to predict future states. For the visual scoring, we experiment with different similarity measures to calculate distance based on entity appearances, including a convolutional neural network (CNN) encoder, pre-trained using Siamese networks. In initial evaluation experiments, we show that our method, combining scoring structure of the kinematic and visual models within a MHT framework, has improved performance especially in edge cases where entity motion is unpredictable, or the data presents frames with significant gaps.

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Occlusion Handling in Tracking Multiple People Using RNN

Cited by 27 publications

References 19 publications

Deep learning in video multi-object tracking: A survey

Deep learning in video multi-object tracking: A survey

Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder

Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications

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