Object tracking under large motion: Combining coarse-to-fine search with superpixels

Kim, Chansu; Song, Donghui; Park, Sung Kee

doi:10.1016/j.ins.2018.12.042

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…By comparing the similarity between the HSV histograms of the rectangle box on the particle and the template bounding box on the human target, this weight is evaluated. This similarity is calculated by using the Hellinger distance as follows [41]:…”

Section: Particle Filter Tracking Algorithmmentioning

confidence: 99%

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Nguyen,

Chu

2023

Journal of Robotics

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Speed and accuracy are important parts of the human tracking system. To design a system that tracks the target human working well in real time, as well as on mobile devices, a tracking real-time target human system is proposed. First, real-time human detection is performed by the combination of MobileNet-v2 and single-shot multibox detector (SSD). Subsequently, the particle filter algorithm is applied to track the target human. The proposed system is evaluated with the different color shirts and complex background conditions. In addition, the system also works with the support of a depth Kinect-v2 camera to evaluate performance. The experiment result indicates that the proposed system is efficient without the impact of colors, background, and light. Moreover, the system still tracks the human when the human has disappeared or the size of the target has a significant change, and an FPS of 12 (Kinect-v2 camera) and 22 (conventional camera) ensures the system works well in real time.

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Section: Particle Filter Tracking Algorithmmentioning

confidence: 99%

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Nguyen,

Chu

2023

Journal of Robotics

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“…In the last decade, optical flow has become an alternative technique to estimate successive frames. In fact, optical flow is widely used in motion detection, robotics, visual navigation, and image processing [15,16]. Moreover, an important amount of research is based on optical flow estimation for vision systems.…”

Section: Optical Flow Overviewmentioning

confidence: 99%

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

2021

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Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

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“…In this method, a certain number of key frames are extracted according to the user in advance, and the video content is extracted according to the original frame sequence every fixed frame to obtain uniform extraction results. Kim et al [14] combined color distribution with the use of motion attributes. Since this method is not an adaptive algorithm, many parameters need to be set artificially.…”

Section: Relevant Workmentioning

confidence: 99%

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

Wang

Zhu

2020

IEEE Access

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In this paper, a motion vector-based video key frame detection algorithm is proposed to solve the problem of miss election and missing selection caused by the difficulty in detecting the moving target characteristics of the video key frame. Firstly, the entropy of adjacent frame difference and the twodimensional entropy of image are introduced, and the combination of the two is taken as the measurement of the difference between video frames. Secondly, outliers are detected by statistical tools to obtain the lens boundary, thus realizing the adaptive lens detection of video content. Then, ViBe algorithm is used to detect the foreground object in the video sequence and extract the scale-invariant feature transformation features of the foreground moving object. Finally, the motion vector is introduced, and the sum of the block matching results is motion vector by partitioning the two adjacent frames and performing block matching. The magnitude of motion vector reflects the intensity of motion in the video, so active and inactive motion regions are obtained, and the similarity of video frames is calculated according to the defined formula, and key frames are extracted in these regions respectively. The experimental results show that the detection algorithm proposed in this paper improves the video results with rich motion information obviously, and the objective indexes and subjective scores are improved to some extent, which improves the universality of the algorithm. In addition, this paper also studies the display mode of key frame extraction results in virtual reality environment. The key frame display mode of video in virtual reality is optimized mainly by changing the display mode of information and changing the scene and testing the result of user task execution.INDEX TERMS Video; key Frame; monitoring algorithm; virtual reality display; motion vector.

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Object tracking under large motion: Combining coarse-to-fine search with superpixels

Cited by 12 publications

References 29 publications

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

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