Online multi-person tracking-by-detection method using ACF and particle filter

Kokul, T.; Ramanan, Amirthalingam; Pinidiyaarachchi, U.A.J.

doi:10.1109/intelcis.2015.7397272

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…Machine learning algorithms focus on feature extraction and classifiers [92]. For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for ...…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…RNNs was used to predict the object position and manage the trajectory, while LSTMs was used to achieve data association. T. Kokul et al [15] proposed an online pedestrian tracking algorithm based on aggregate channel features (ACF) and particle filter. Each object was detected by the ACF detector, and then the Adaboost classifier was used to train the object and background.…”

Section: Related Workmentioning

confidence: 99%

“…The contour coefficient method is used to analyze the objective function to select the optimal clustering number K. When K is 12, the objective function is optimal. The obtained anchor boxes are (183, 142), (71, 188), (101, 70), (29, 23), (24, 53), (54, 38), (12, 10), (9,21), (20,15), (6, 10), (9, 6) and (5,5).…”

Section: Figure 4: the Detection Networkmentioning

confidence: 99%

A Real-Time Multiple Object Tracking Network Under Complex Traffic Environment

2022

IJOMAM

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Multiple object tracking is extremely important in autonomous driving. Most multiple object tracking methods include two steps: object detection and data association. The first step is to detect each frame object in the video. The second step is to establish the relationship between video frames and to obtain the object trajectory. In this study, the M-YOLOV5s is proposed to train the improved BDD100K datasets based on the YOLOV5s. Then, the Hungarian algorithm is used to match the trajectory predicted by Kalman filtering with the detection object. The corresponding objects between the video frames are found and the multiple objects are tracked in real-time by constructing the similarity matrix. The experiment results on the MOT16 benchmark show that the whole algorithm has strong robustness and can achieve multiple object tracking under complex traffic environments.

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“…Based on the features used in a tracking model, VOT approaches can be categorized as hand-crafted and deep feature-based trackers. Hand-crafted feature-based tracking approaches [11,12,13,14,15] extract the features from images according to a certain manually predefined algorithm based on expert knowledge. On the other hand, deep feature-based trackers [16,17,18,19] capture the semantic cues from raw images by using the Convolutional neural networks (CNNs) [20].…”

Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Thangavel¹,

Thanikasalam²,

Ramanan³

et al. 2023

Preprint

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Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.

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Online multi-person tracking-by-detection method using ACF and particle filter

Cited by 9 publications

References 20 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

A Real-Time Multiple Object Tracking Network Under Complex Traffic Environment

Transformers in Single Object Tracking: An Experimental Survey

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