Moving object detection and tracking from video captured by moving camera

Hu, Wu-Chih; Chen, Chao-Ho; Chen, Tsong-Yi; Huang, Deng-Yuan; Wu, Zong-Che

doi:10.1016/j.jvcir.2015.03.003

Cited by 92 publications

(57 citation statements)

References 19 publications

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“…Then, graphcut method is applied as energy minimization technique to the likelihood maps for segmentation. Authors of [13] and [5] have extracted the feature points in the frames using standard feature point detection algorithms and classified them as foreground or background points by comparing optical flow features with multiple-view geometry. Foreground regions are obtained through image differencing and integrating classified foreground feature points.…”

Section: A On Spatial Analysismentioning

confidence: 99%

Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

Ray

Chakraborty

2019

Journal of Visual Communication and Image Representation

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In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. The performance of tracking in videos with variable background depends on the successful detection of an object in variable background. The most attractive feature of detecting an object in variable background is that it does not depend on any a priori information of the scene. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin grayvalue histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.

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Section: A On Spatial Analysismentioning

confidence: 99%

Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

Ray

Chakraborty

2019

Journal of Visual Communication and Image Representation

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“…Wang developed the MRF scheme for identifying the moving vehicles under certain weather environments. However, due to its appropriation with the grey‐scale videos only, it causes the limitation in this approach . Hence, to manage the grey values spatial opacities, Ghosh et al developed the motion estimation approach based on the region‐matching for attaining the moving object identification and video tracks from motion‐camera.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to its appropriation with the grey-scale videos only, it causes the limitation in this approach. 37 Hence, to manage the grey values spatial opacities, Ghosh et al 38 developed the motion estimation approach based on the region-matching for attaining the moving object identification and video tracks from motion-camera. Moreover, it has the higher computing cost that made certainly limited utilisations in an offline process and the optimum results are not obtained if there is an existence of occlusion or dis-occlusion and shadows.…”

Section: Related Workmentioning

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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“…On the second scan, the corresponding labels in a connected component were assigned their unique representative labels. For visual surveillance of moving objects, Hu et al [34,35] proposed a fast 4-connected component labeling algorithm to reduce the influence of a dynamic environment, such as wave ripples. In each scan process of this algorithm, the adjacent pixels of two rows were merged into an isolated block, which was labeled with the minimum label value in the block.…”

Section: Related Workmentioning

confidence: 99%

GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance

Song

Tian

Fong³

et al. 2016

Sustainability

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Real-time and accurate background modeling is an important researching topic in the fields of remote monitoring and video surveillance. Meanwhile, effective foreground detection is a preliminary requirement and decision-making basis for sustainable energy management, especially in smart meters. The environment monitoring results provide a decision-making basis for energy-saving strategies. For real-time moving object detection in video, this paper applies a parallel computing technology to develop a feedback foreground-background segmentation method and a parallel connected component labeling (PCCL) algorithm. In the background modeling method, pixel-wise color histograms in graphics processing unit (GPU) memory is generated from sequential images. If a pixel color in the current image does not locate around the peaks of its histogram, it is segmented as a foreground pixel. From the foreground segmentation results, a PCCL algorithm is proposed to cluster the foreground pixels into several groups in order to distinguish separate blobs. Because the noisy spot and sparkle in the foreground segmentation results always contain a small quantity of pixels, the small blobs are removed as noise in order to refine the segmentation results. The proposed GPU-based image processing algorithms are implemented using the compute unified device architecture (CUDA) toolkit. The testing results show a significant enhancement in both speed and accuracy.

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Moving object detection and tracking from video captured by moving camera

Cited by 92 publications

References 19 publications

Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

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

GPU-Accelerated Foreground Segmentation and Labeling for Real-Time Video Surveillance

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