Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

Mandal, Vishal; Adu-Gyamfi, Yaw

doi:10.1007/s42421-020-00025-w

Cited by 43 publications

(9 citation statements)

References 38 publications

(36 reference statements)

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“…We consider the tracking has succeeded, if the OR ⩾ 0.5. Moreover, we get the success ratio (SR) by setting an overlap score r which is defined as the minimum overlap ratio, which can decide whether an output bounding box is correct or not, it is calculated by (24):…”

Section: Discussionmentioning

confidence: 99%

“…Histogram of oriented gradients (HOG) feature [21] were used with correlation filter to learn an adaptive multi-scale. Furthermore, correlation filters framework were combined with deep learning approaches such as convolutional neural networks (CNN) [22], [23], new tracker category utilize Yolo and deep sort (DS) combination to detecte and track the target [24], [25]. Circulant structure of tracking by detection with kernels (CSTDK) tracker proposed in [26] provides sufficient performance and good results in speed processing.…”

Section: Introductionmentioning

confidence: 99%

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Self scale estimation of the tracking window merged with adaptive particle filter tracker

Azdoud

Aouatif²,

Bouchra³

et al. 2023

IJECE

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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Azdoud

Aouatif²,

Bouchra³

et al. 2023

IJECE

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show abstract

“…For instance, recognition of road signals in poor environmental conditions has been studied by Temel et al (2020). A comparative analysis of several stateof-the-art object detection and tracking algorithms to detect and track different classes of road vehicles is presented in Mandal and Adu-Gyamfi (2020). Further, many open datasets are available for researchers working on autonomous vehicles (Harb et al 2020).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

Staino

Suwalka

Mitra

et al. 2022

J. Big Data Anal. Transp.

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Automated detection and recognition of traffic signals are of great significance in railway systems. Autonomous driving solutions are well established for urban rail transportation systems. Many metro lines in service worldwide have reached the highest grade of automation where the train is automatically operated without any staff on board. However, autonomous driving is still an open challenge for mainline trains, due to the complexity of the mainline environment. In this context, automated recognition of wayside signals can help to minimise the risk of human error owing to low visibility and fatigue. It represents a key step towards the fully autonomous train. In this article we present a deep learning based approach for the above task. The You Only Look Once (YOLOv5) is used for detection and recognition of wayside signals. A heuristic is used to recognise blinking states. We consider FRSign dataset, a large collection of over 100,000 images of traffic signals from some of the trains in French Railways. A distilled and cleaned version of the dataset curated by us is used for training. The trained network has low computational overhead and can recognise traffic signals in real time and under diverse field conditions. It has robust performance even for complex scenes having multiple signals and light sources, and in adverse circumstances such as rain and night environments. The refined version of the dataset is published as open for validation and further research and development.

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“…These approaches typically employ a tracking-by-detection pipeline followed by counting based on either region-of-interest (ROI) [25,86] or line-of-interest (LOI) [73,85,151]. ROI-based methods attempt to estimate the number of objects passing through a subregion of the frame, such as a traffic lane or onramp in the case of vehicle counting [25].…”

Section: Related Workmentioning

confidence: 99%

The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

Kay¹,

Kulits²,

Stathatos³

et al. 2022

Preprint

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We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.

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Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

Cited by 43 publications

References 38 publications

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

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