Dense optical flow based background subtraction technique for object segmentation in moving camera environment

Kushwaha, Arati; Khare, Ashish; Prakash, Om; Khare, Manish

doi:10.1049/iet-ipr.2019.0960

Cited by 19 publications

(8 citation statements)

References 40 publications

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“…In this study, the optical flow estimation will be obtained according to the relevant principles in the personalized learning algorithm [19,20]. For the displacement feature point y of layer i in the fall action image Q not recognized in the attitude data sequence, the corresponding feature point of layer i in the preset action image R is m. Using the expression set above, it is necessary to determine the value range of optical flow vector ∂ [21], so that:…”

Section: Realize Human Fall Motion Recognitionmentioning

confidence: 99%

Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

Hui¹,

Jun²,

Na³

2022

Journal of Sensors

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In order to improve the recognition accuracy of human falling actions, the impact of randomness of actions is reduced. To this end, this paper proposes an automatic recognition method for physical fitness human fall based on pose data sequence. The color camera is used to collect the fall motion images of the physical fitness personnel, and the motion image preprocessing is completed by extracting the fall motion features of the human body, tracking and adjusting the fall motion of the human body. A model of human body fall movement displacement feature extraction from posture data sequence is constructed. By tracking the displacement feature points, the automatic recognition of physical fitness human body fall movement based on posture data sequence is realized. The experimental results confirm that the proposed method can effectively obtain the details of the fall motion images of physical fitness. When the number of human falling actions reaches 500, the accuracy of action recognition is also as high as 77%, improving the recognition effect of human fall action.

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Section: Realize Human Fall Motion Recognitionmentioning

confidence: 99%

Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

Hui¹,

Jun²,

Na³

2022

Journal of Sensors

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“…Currently, SLAM systems that adopt three solutions in dynamic environments can be divided into two categories, namely the SLAM systems based on camera motion [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] and the SLAM systems independent of camera motion [ 26 , 27 , 28 ]. The former systems need to first calculate the motion of the camera itself, and then judge and distinguish the dynamic target.…”

Section: Related Workmentioning

confidence: 99%

DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow

Long

Ding

2022

Biomimetics

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Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively.

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“…It can be considered that the change of the moving target is obvious. Given a threshold, when the difference of a pixel value in the differential image is greater than a given threshold, the pixel is considered to be a foreground pixel, possibly a point on the target; otherwise, it is considered a background pixel [32][33][34].…”

Section: Extracting the Goal Of Basketball Shooting Trainingmentioning

confidence: 99%

Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

Guo

Hui

2021

Scientific Programming

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Aiming at the difficulty of standardizing the action of basketball shooting training, a new method of standardizing the action of basketball shooting training is proposed based on digital video technology. The digital video signal representation, video sequence coding data structure, and video sequence compression coding method are analyzed, and the pixels of basketball shooting training action position space are sampled to collect basketball shooting training images. The time difference method is used to extract the movement target of basketball shooting training from a digital video sequence. Based on digital video technology, the initial background image is estimated, and the update rate is introduced to update the background estimation image. According to the pixel value sequence of the basketball shooting training image, the pixel model of the basketball shooting training image is defined and modified. By judging whether the defined pixel value matches the background parameter model, the standardization of shooting training can be realized. The experimental results show that the proposed method has good stability, high precision, and short time in determining the standardization of shooting movement, can correct the wrong shooting movement in real time, and can effectively guide basketball shooting training.

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Dense optical flow based background subtraction technique for object segmentation in moving camera environment

Cited by 19 publications

References 40 publications

Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

Automatic Recognition Method of Fall Movement of Sports Fitness Human Body Based on Posture Data Sequence

DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow

Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

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