Real-time multi-object detection and tracking for autonomous robots in uncontrolled environments

Said, Tarek; Ghoniemy, Samy; Karam, Omar H.

doi:10.1109/icces.2012.6408485

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…Multiple object tracking (MOT) is one of the most important problem in computer vision and has applications in areas of autonomous robotics [20,50], autonomous driving [12,24,43,52] and smart cities [8,44,52,72]. The problem consists of determining the position and identity of each object of interest (e.g.…”

Section: Introductionmentioning

confidence: 99%

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Stanojevic,

Todorovic

2024

Machine Vision and Applications

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Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce no identity switches. To approach the described ideal solution, we present the BoostTrack, a simple yet effective tracing-by-detection MOT method that utilizes several lightweight plug and play additions to improve MOT performance. We design a detection-tracklet confidence score and use it to scale the similarity measure and implicitly favour high detection confidence and high tracklet confidence pairs in one-stage association. To reduce the ambiguity arising from using intersection over union (IoU), we propose a novel Mahalanobis distance and shape similarity additions to boost the overall similarity measure. To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object. The proposed additions are orthogonal to the existing approaches, and we combine them with interpolation and camera motion compensation to achieve results comparable to the standard benchmark solutions while retaining real-time execution speed. When combined with appearance similarity, our method outperforms all standard benchmark solutions on MOT17 and MOT20 datasets. It ranks first among online methods in HOTA metric in the MOT Challenge on MOT17 and MOT20 test sets. We make our code available at https://github.com/vukasin-stanojevic/BoostTrack.

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Section: Introductionmentioning

confidence: 99%

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Stanojevic,

Todorovic

2024

Machine Vision and Applications

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“…The work reported in [164] is another example of an MTTVS system developed for real-time detection and tracking of multiple objects in uncontrolled environments. The multi-object tracking model is designed with a multithreading architecture, where every thread corresponds to a detected motion area and integrates motion and color tracking.…”

Section: Multi-target Tracking Systemsmentioning

confidence: 99%

Classical and Deep Learning based Visual Servoing Systems: a Survey on State of the Art

Machkour¹,

Ortiz-Arroyo²,

Løhndorf³

2021

J Intell Robot Syst

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Computer vision, together with bayesian estimation algorithms, sensors and actuators are used in robotics to solve a variety of critical tasks such as localization, obstacle avoidance, and navigation. Visual servoing uses computer vision algorithms to guide robot movements. Classical approaches in visual servoing systems relied on extracting features from images to control robot movements. Now, state of the art computer vision systems use deep neural networks for object recognition, detection, segmentation, and tracking. These networks and specialized controllers play a predominant role in the design and implementation of modern visual servoing systems due to their accuracy, flexibility, and adaptability. Recent research in direct systems for visual servoing has created robotic systems that rely only on the information extracted from images. Furthermore, end-to-end systems eliminate entirely the controller by learning the control laws during training.This paper presents a comprehensive survey on the state of the art in visual servoing systems, discussing the latest classical methods not included in other surveys but emphasizing the new approaches based on deep neural networks and their applications within robotics.

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“…Black color will absorb the beam falling on it completely. [11] The crucial element of our work was programming. The initial part o f the code is to control the movement of the vehicle to the front, back, left, and right.…”

Section: Designing Of Internal and Externa L Sections Of Vehiclementioning

confidence: 99%

Design and Implementation of Automatic Guided Vehicle for Hospital Application

Antony¹,

Parameswaran²,

Mathew³

et al. 2020

2020 5th International Conference on Communication and Electronics Systems (ICCES)

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With the advancements of robotic technologies, the medical environments are adopting more and more as pects of automation to enhance the services in hos pitals. In pandemic conditions such as COVID 19, direct contact with patients may result in the s preadi ng of disease. Hence the health community finds difficulty in distri buting medicines and disposal of waste. The design and development of an automatic gui ded vehicle for hos pital applicati ons, which can be controlled remotely is specified here. AGV is a line follower robot, powered by a battery that can be charged from solar energy. It has infrared sensors at the bottom for path i dentification and an ultrasonic sensor hel d i n front of the vehicle for obstacle detection. It collects medicines from the nurse's station and follows the path provi ded to reach the suppl y point, which is the patient's bed. The sensors provi ded will detect the bed and opens the correspondi ng box of medicine. In addi tion to this, it hel ps i n real-time monitoring of patient's parameters such as temperature, pulse rate, etc. and sends informati on to the doctor through the internet. AGV also collects waste from the bi n and dump it to a proper place.

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Real-time multi-object detection and tracking for autonomous robots in uncontrolled environments

Cited by 8 publications

References 21 publications

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Classical and Deep Learning based Visual Servoing Systems: a Survey on State of the Art

Design and Implementation of Automatic Guided Vehicle for Hospital Application

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