Target Tracking via Particle Filter and Convolutional Network

Chu, Hongxia; Wang, Kejun; Xing, Xiaolei

doi:10.1155/2018/5381962

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…The main features of this filter are its simplicity and its flexibility. It is very easy to handle non-Gaussian and multimodality system models with this filter but it does not work well with gaussian systems, which is a drawback [31].…”

Section: Object Detection Tracking Techniquesmentioning

confidence: 99%

SSDT: Distance Tracking Model Based on Deep Learning

Gupta

Gill

Gulia

2022

Int. j. electr. comput. eng. syst. (Online)

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Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.

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Section: Object Detection Tracking Techniquesmentioning

confidence: 99%

SSDT: Distance Tracking Model Based on Deep Learning

Gupta

Gill

Gulia

2022

Int. j. electr. comput. eng. syst. (Online)

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“…Robot-based detection technology [6] and UAV-based detection technology [7,8] both need to track the determined target, so the robust tracking method is of great importance. Deep learning provides strong support for the development of more robust target tracking [9,10]. However, the deep features provided by the deep neural network model have more parameters, so it is very expensive in computing and storage, which makes it difficult to meet the real-time requirements of intelligent inspection and difficult to deploy on mobile intelligent inspection devices.…”

Section: Introductionmentioning

confidence: 99%

Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking

Jiang

Guo²,

Zhang

et al. 2022

Journal of Electrical and Computer Engineering

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Deep learning has brought revolutionary progress to computer vision, so intelligent inspection equipment based on computer vision has developed rapidly. However, due to the large number of existing deep features, it is difficult to deploy it on mobile devices to achieve real-time tracking speed. This paper presents a target-aware deep feature compression for power intelligent inspection tracking. First, a negative balance loss function is designed to mine channel features suitable for the current inspection scene by shrinking the contribution of pure background negative samples and enhancing the impact of difficult negative samples. Based on this, the deep feature compression model is combined with Siamese tracking framework to achieve real-time and robust tracking. Finally, we evaluate the proposed method on real application scenarios and general data to prove the practicability of the proposed method.

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“…Popular models can typically incorporate some form of generic measurement noise, yet major disturbances, such as occlusions, pose a challenge. Hence, it is a matter of ongoing research as to how to design robust tracking schemes that enable a re-detection of the object after it has been lost and realize a larger robustness and invariance to noise, as an example, by means of multiple correlation filters or robust representations, such as hidden layers of deep networks [15][16][17]. However, in realistic scenarios, it is unavoidable that tracked objects are lost in some settings, as an example, in the case of a total occlusion of the tracked object.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Kummert

Schulz

Redick

et al. 2021

Sensors

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Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain—object tracking in assisted surgery in the domain of Robotic Osteotomies—that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.

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Target Tracking via Particle Filter and Convolutional Network

Cited by 7 publications

References 24 publications

SSDT: Distance Tracking Model Based on Deep Learning

SSDT: Distance Tracking Model Based on Deep Learning

Target-Aware Deep Feature Compression for Power Intelligent Inspection Tracking

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

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