Moving vehicle tracking based on improved tracking–learning–detection algorithm

Dong, Enzeng; Deng, Mengtao; Tong, Jigang; Chen, Jia; Du, Shengzhi

doi:10.1049/iet-cvi.2018.5787

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…However, for the use of UAV to track dynamic objects, the inertia and external disturbance generated during the flight of UAV make the object have large position changes on the adjacent frame images, and it is unable to generate motion information with certain rules to provide prediction, which will cause inaccurate object prediction position, thus losing the object. Given such problems, researchers combine image registration technology with the traditional moving object trajectory prediction method, to locate the moving object more accurately [2][3] . However, under the airborne aerial photography task with disturbance, the continuous switching method may lead to an unnecessary computational burden.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Zhou,

Yao,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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Compared with the traditional methods based on the prediction of moving object trajectory, the method based on image registration has the advantage of not relying on the object motion equation to track the object. However, in the case of a large number of outliers, the image matching method has the defects of inadequate filtering of outliers or erroneous filtering of incomers due to the limitations of spatial constraints of feature matching pairs, which will make the tracking of flying objects inaccurate or impossible. During the flight, the object may be in a similar background and the image shooting angle is different, which makes the object imaging angle change, resulting in a large number of outlier interference. Because of the above situation, this paper adopted an improved TRESAC algorithm based on RANSAC, which used a feature matching method based on a triplet relationship to effectively filter outliers and then adopted an initial data subset selection strategy to increase its robustness. Experimental results show that the TRESAC algorithm can filter outliers quickly and accurately, and the object is still tracked effectively under the condition of similar background and pose change.

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Section: Introductionmentioning

confidence: 99%

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Zhou,

Yao,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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“…As a fundamental and important problem in computer vision, object detection has been continuously investigated in the past decades. With the development of deep learning in recent years, object detection has made great progression and been widely applied in various real‐world applications, such as vehicle detection [1–4], pedestrian detection [5–9], intelligent monitoring [10–13], etc.…”

Section: Introductionmentioning

confidence: 99%

A multi‐scale feature representation and interaction network for underwater object detection

Yuan

Sun

et al. 2022

IET Computer Vision

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Compared with natural images, underwater images are usually degraded with blur, scale variation, colour shift and texture distortion, which bring much challenge for computer vision tasks like object detection. In this case, generic object detection methods usually fail to achieve satisfactory performance. The main reason is considered that the current methods lack sufficient discriminativeness of feature representation for the degraded underwater images. A a novel multi-scale feature representation and interaction network for underwater object detection is proposed, in which two core modules are elaborately designed to enhance the discriminativeness of feature representation for underwater images. The first is the Context Integration Module, which extracts rich context information from high-level features and is integrated with the feature pyramid network to enhance the feature representation in a multi-scale way. The second is the Dual-refined Attention Interaction Module, which further enhances the feature representation by sufficient interactions between different levels of features both in channel and spatial domains based on attention mechanism. The proposed model is evaluated on four public underwater datasets. The experimental results compared with state-of-the-art object detection methods show that the proposed model has leading performance, which verifies that it is effective for underwater object detection. In addition, object detection experiments on a foggy dataset of Real-world Task-driven Testing Set (RTTS) and the natural image dataset of pattern analysis statistical modelling and computational learning, visual object classes (PASCAL VOC) are conducted. The results show that the proposed model can be applied on the degraded dataset of RTTS but fails on PASCAL VOC. K E Y W O R D S computer vision, convolutional neural nets, object detection | INTRODUCTIONAs a fundamental and important problem in computer vision, object detection has been continuously investigated in the past decades. With the development of deep learning in recent years, object detection has made great progression and been widely applied in various real-world applications, such as vehicle detection [1-4], pedestrian detection [5-9], intelligent monitoring [10-13], etc.Although the generic object detection algorithms have promising performance in most cases, they often fail to achieve convincing results under some specific scenes, such as underwater, foggy and dark light, etc. Compared with the common scenarios, the object detection in these specific scenes faces much challenge and difficulty to overcome, including image blur, texture distortion, imbalanced illumination, etc. For this purpose, many researchers began and focussed on the object detection in specific scenes. For example, underwater object detection [14] has raised much attention recently, as it is an important and common problem in the fields of seafood industry, underwater engineering and ocean development. Specifically, for the aquaculture industry, underwater robots ...

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“…Dong et al used the single-target long-time algorithm to track, learn, and monitor the moving vehicle for a long time from the video stream. The problem was found that caused the tracking failure in the algorithm, and an improved tracking algorithm was proposed [6]. The overspeed of some vehicles becomes an obstacle of tracking.…”

Section: Introductionmentioning

confidence: 99%

Migration Path Tracking Algorithm of Egret Birds Based on Intelligent Remote Sensing Monitoring

2022

Wireless Communications and Mobile Computing

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There are billions of migratory birds migrating between breeding grounds and wintering grounds in the world every year. Egret is no exception. During the migration period, they need to travel for a long time and long distances. At present, the protection strategies of egrets and the dynamic mechanism of migration routes are concerned by experts in related fields. Researchers generally explore the above problems by tracking the migration paths of egrets. Most of the existing path tracking algorithms use some classical tracking algorithms, but the tracking accuracy of the classical algorithms is low. Therefore, this paper has applied the intelligent remote sensing monitoring technology to the improvement of the path tracking algorithm, used artificial intelligence technology to remove the redundant information of the image, and combined the Kalman filter with the single-target long-term algorithm to improve the tracking algorithm. 100 samples were simulated, and the experimental results showed that the tracking accuracy of the egret bird migration path tracking algorithm based on intelligent remote sensing monitoring was improved by 8.37% compared with the algorithm before improvement, which has better utilization value.

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Moving vehicle tracking based on improved tracking–learning–detection algorithm

Cited by 17 publications

References 37 publications

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

A multi‐scale feature representation and interaction network for underwater object detection

Migration Path Tracking Algorithm of Egret Birds Based on Intelligent Remote Sensing Monitoring

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