Moving object detection using background subtraction and motion depth detection in depth image sequences

Lee, Jichan; Lim, Sung-Soo; Kim, Jun-Geon; Kim, Bomin; Lee, Daeho

doi:10.1109/isce.2014.6884308

Cited by 2 publications

(4 citation statements)

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“…Iwata et al [12] developed a human-following robot to track the human back and shoulders. Another type of object-tracking robot design uses a vision-based algorithm to identify the colour and texture of an object [13]. Warren E. Dixon et al [14] designed a position-based tracking control for a wheeled mobile robot using an uncalibrated camera system.…”

Section: Related Workmentioning

confidence: 99%

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Design and implementation of a computer Vision-based Object Tracking Robot using an Uncalibrated Camera

Nitu,

Barman,

Kumar

2024

Preprint

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Object detection and tracking are challenging tasks in computer vision applications, such as autonomous robot navigation, vehicle navigation, human-computer interaction, augmented reality and defence surveillance systems. Detecting and tracking an object, especially for humans and vehicles, is a challenging research topic in computer vision. This paper represents an autonomous object-tracking robot using a computer vision object detection and tracking algorithm with a novel robot controller. The speeded-up-robust-feature (SURF) based object detection technique has been employed for object detection, and Kanade-Lucas-Tomasi (KLT) has been used for object tracking. The stereo vision-based depth measurement technique measures objects' depth. Based on the object's location in 3D space, the robot controller navigated the robot to track the object. For this purpose, the robot controller is designed with Arduino and L293D motor driver IC. The robot's speed was controlled using a pulse width modulation (PWM) speed control algorithm. The simulated results show that the proposed techniques efficiently track objects in 3-D space.

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Section: Related Workmentioning

confidence: 99%

“…This control technique overcomes the problem of camera motion, scaling, and pose changes [7]. An uncalibrated stereo camera is used for object detection and tracking to overcome the camera calibration problem [8]. The PWM speed control technique is used to control the robot's speed.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a computer Vision-based Object Tracking Robot using an Uncalibrated Camera

Nitu,

Barman,

Kumar

2024

Preprint

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“…23 Identification of foreground is crucial to distinguish moving object from the stationary background. 24 Following extraction, acquired images from video frames were pre-processed based on source 25 and subjected to enhancement using morphological processing, which details out or highlights particular features of interest in the image and certain obscure images. 25 Such conventional methods often had its setbacks due to the presence of dynamic scene changes, variation in illumination and colour, the presence of shadow, noise, etc.…”

Section: Of 10mentioning

confidence: 99%

“…Conventionally, recorded video sequence from the fixed or stationary camera was first extracted into video frames, and detection of moving object was based on the identification of foreground of the moving object . Identification of foreground is crucial to distinguish moving object from the stationary background . Following extraction, acquired images from video frames were pre‐processed based on source and subjected to enhancement using morphological processing, which details out or highlights particular features of interest in the image and certain obscure images .…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of colour texture‐based multiple object detection using morphological gradient approach

Kandavalli

Lincon

2018

Concurrency and Computation

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Summary Non‐rigid moving multiple objects detection and tracking play an important role in intelligent video surveillance system, autonomous navigation, and activity analysis. Closed Circuit Television (CCTV) systems are deployed in numerous areas such as airports, traffic intersections, underground stations, mass events, mall, schools, and organisations for security and public surveillance. Although these cameras record continuous video 24x7, it is a human constraint to manually monitor all events such as crime, terrorism, hideous, suspicious activities, the positioning of the vehicle, and fire recorded from a number of cameras. Moreover, problems like dynamic background, the creation of ghost, sensor noise, varying illumination, and colour and compression artefacts affect effective detection of multiple moving objects. This study presents an effective approach named as enhanced Fractal Texture Analysis with KNN classifier (FTAKC) for tracking and detection of multiple objects from a video sequence. The proposed approach comprises three main phases, namely, detection of moving object, tracking of the object (enhanced Fractal Texture Analysis), and behaviour analysis for activity recognition (KNN classifier). The image feature has been extracted based on colour, texture, and geometry were used to identify and track multiple objects in video frames, and Problem domain knowledge rules were applied to distinguish normal or anomalous activities as well as behaviours. Edge detection algorithm (Intersection over Union (IoU) threshold to determine possible edge connections) was applied toward enhancing the illumination variation by multi‐block Local Binary Pattern (LBP) temporal‐analysis to do the segmentation. Finally, the efficiency and effectiveness of the proposed approach has been estimated based on the measure of average PSNR, precision, recall, f‐measure, accuracy, and execution time. The Laboratory for Image and Media Understanding (LIMU) dataset has been utilised toward illustrating the robustness of the proposed approach. Furthermore, it evaluated the performance based on the measure of precision, recall, and F‐measure metrics. It has been tentatively demonstrated that the proposed approach is suitable for recognizing multiple moving object with detection accuracy up to 93.56%. The simulated results show that suggested approach is robust, flexible, as well as able to outperform the traditional methods than the present object detection method.

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Moving object detection using background subtraction and motion depth detection in depth image sequences

Cited by 2 publications

References 4 publications

Design and implementation of a computer Vision-based Object Tracking Robot using an Uncalibrated Camera

Design and implementation of a computer Vision-based Object Tracking Robot using an Uncalibrated Camera

Design and implementation of colour texture‐based multiple object detection using morphological gradient approach

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