Online Multi-Object Tracking With Visual and Radar Features

Bae, Seung Hwan

doi:10.1109/access.2020.2994000

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…Tracking-by-detection approaches [13,14,15,16] separate detection and tracking steps. These approaches deal only with the current frame and usually apply popular off-shelf detector networks to generate detection bounding boxes for objects of interest, such as DPM [17], Faster-RCNN [18] and SDP [19].…”

Section: Related Workmentioning

confidence: 99%

“…Afterward, DeepSORT [14] extends the SORT [13] approach by extracting distinct features after detecting the objects of interest to help in their re-identification through time. Moreover, Bae [15] proposed a tracking framework that models objects' visual and radar features and their affinity using a confidence-based data association model and a visual learning object model. Alternatively, Liang et al [16] use graph neighbor networks to model full contextual relations for each tracklet with its surrounding neighbor tracklets for effective data association.…”

Section: Related Workmentioning

confidence: 99%

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LMOT: Efficient Light-Weight Detection and Tracking in Crowds

2022

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Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-theart trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model. INDEX TERMSMulti-object tracking, pedestrian tracking, joint detection and tracking, object detection, deep learning.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

LMOT: Efficient Light-Weight Detection and Tracking in Crowds

2022

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“…Need system for transferring information from UE to BS [20] OTDOA Limited accuracy (10 m >) [31], [32], [33] Tracking with vision and mmWave radar…”

Section: B Object Trackingmentioning

confidence: 99%

“…Methods for tracking objects by combining images and radio information have been studied. Most are sensor fusion techniques that use a camera and mmWave radar [31], [32], [33]. Images from a camera and point clouds from mmWave radar are aligned, and detection and tracking based on the respective data are extrinsically merged.…”

Section: Proposed Tracking With Vision and Rssimentioning

confidence: 99%

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

Mihara

Murakami

Yamaguchi

et al. 2022

IEEE Open J. Commun. Soc.

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In a mobile millimeter wave (mmWave) communication system, blockages cause disconnections or serious degradation of communications. Several techniques have been proposed to control radio links between multiple base stations based on blockage prediction using camera images to avoid these problems. However, blockage prediction requires continuously determining the position of user equipment (UE) with decimeter precision, which is difficult when sensors and resources on the UE side are not available, and there are many moving objects around the UE. To resolve this problem, we propose a UE tracking method that uses a received signal strength indicator (RSSI) and RGB-D camera images from the base station. The proposed method consists of a combination of visual tracking and reidentification of the UE using radio information and camera images. We use the synchronization of the RSSI variation with the occlusion on the image by movements of the UE and objects for reidentification. We evaluated the proposed method experimentally in an outdoor environment by simulating a communication area formed by mmWave band base stations. The proposed method achieved an 11.5% improvement in tracking accuracy compared with conventional visual tracking.INDEX TERMS Image analysis, millimeter wave communication, object tracking,

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“…It has many applications, including surveillance, robotics, and sport analysis and human-computer interaction. The task of MOT is divided into three parts: locating multiple objects, maintaining their identities and the most important one is yielding their individual trajectories given an input video [1][2][3]. Tracking techniques can be categorized as follows: the batch method and the online method [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Online multi‐object tracking based on time and frequency domain features

Nazarloo

Tabari

Motameni

2021

IET Computers & Digital Tech

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Multi-object tracking (MOT) can be considered as an interesting field in computer vision research. Its application can be found in video motion analysis, smart interfaces, and visual surveillance. It is a challenging issue due to difficulties made by a variable number of objects and interaction between them. In this work, a new method for online MOT based on time and frequency domain features is presented. The features are obtained from the wavelet transform and fractal dimension. The modified cuckoo optimization algorithm is utilized for feature selection, which has the ability such as fast convergence and global optima finding. The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. To evaluate the performance of the presented technique, simulations are performed using the ETH Mobile Platform and VS-PETS 2009 datasets. The simulation results show the superiority of the presented technique for MOT compared to earlier studies in terms of accuracy. The mostly tracked values for the datasets are 74.3% and 97.2%, which leads to at least 4.2% and 2.5% better performance according to the other methods, respectively. K E Y W O R D S fractal dimension, learning vector quantization, modified cuckoo optimization algorithm, multi-object tracking, time and frequency domain features, wavelet transform This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Online Multi-Object Tracking With Visual and Radar Features

Cited by 11 publications

References 39 publications

LMOT: Efficient Light-Weight Detection and Tracking in Crowds

LMOT: Efficient Light-Weight Detection and Tracking in Crowds

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

Online multi‐object tracking based on time and frequency domain features

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