Moving Object Detection Using Background Subtraction

Shaikh, Soharab Hossain; Saeed, Khalid; Chaki, Nabendu

doi:10.1007/978-3-319-07386-6

Cited by 52 publications

(32 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The change with respect to the background model denotes the moving obcect. The background frame without any obcect is captured, afterward, when a moving obcect enters, the second picture is formed [2]. Subtracting the second frame from the first background frame gives the dissimilarity between two frames and the position of moving obcect can be obtained as shown in Figure 3.…”

Section: Optical Fl Owmentioning

confidence: 99%

“…The issues related to obcect tracking like occlusion, view through multiple cameras are also discussed. Background subtraction is a widely-used approach for detecting moving obcects from static cameras [2]. Humans can easily detect the obcects present in an image or video.…”

Section: Related Work On Object Trackingmentioning

confidence: 99%

“…The obcect detection techniques used are frame differencing, optical flow and background subtraction. Out of three macor classes of moving obcect detection techniques, namely, frame differencing, optical flow, and background subtraction, the last is somewhat robust, as compared to the others [1] [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar¹,

Kamble²,

Thakur³

et al. 2017

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

-Object tracking is one of the vital fields of computer vision that detects the moving object from a video sequence. Object detection is used to detect the object present in the video and to find the exact location of that object. The object tracking can be applied in various fields that include video surveillance, robot vision, traffic monitoring, automated civil or military surveillance system, traffic monitoring, human-computer interaction, vehicle navigation, biomedical image analysis, medical imaging and much more. The object tracking algorithm requires tracking the object in each frame of the video. A common approach is to use the background subtraction, which eliminates the common static background, resulting into foreground region showing the presence of the desired object. Block matching technique is the most popular technique for computing the motion vectors between the two frames of video sequences and different searching techniques are available to compute motion vectors between frames. Still, there is a scope for improvement in modifying or developing a new shape pattern for block matching motion estimation to find out and track the object in the video. This paper presents the several object detection and tracking methods and how block matching can be used to track object from a video.

show abstract

Section: Optical Fl Owmentioning

confidence: 99%

Section: Related Work On Object Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar¹,

Kamble²,

Thakur³

et al. 2017

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Until today, it is still a great challenge to achieve an accurate, robust and high performance approach. How can one define the object to be detected and tracked is the difficulty level of this problem Shaikh et al (2014), which help to make the decision for determining the path of Autonomous Cars and avoid accidents.…”

Section: Introductionmentioning

confidence: 99%

Detection of a Moving Car Based on Invariant Moments

Rahma¹,

Basil²

2018

Journal of Computer Science

View full text Add to dashboard Cite

This paper presents a proposed technique based on Invariant Moments to detect moving cars in front of Autonomous Cars in order to increase safety and reduce road accidents, thus saving lives, which are one of the most important matters in the context of Autonomous Cars. Object detection from a sequence of images is preparatory step but a crucial mission for computer vision applications in information extraction. In this work, a car database was constructed and used in background subtraction to define Region of Interest, then applying the invariant moments for RoI and dependence on the information of the database that was constructed to detect and recognize the moving car. The technique was tested and the experimental results showed that it could detect the moving objects successfully with detection rate over 87% and precision 97% and FoM 91%.

show abstract

“…The object detection mechanism is carried out either in the sequence of frames or when the object first appears in the frame of the video for tracking [1,2]. Background subtraction is a process of subtracting the second frame (current frame) from the first background (reference) frame, thus the dissimilarity between two frames and the position of moving object can be obtained [3,4]. Object tracking means the process of locating the object of interest from a video sequence.…”

mentioning

confidence: 99%

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Samdurkar¹,

Kamble²,

Thakur³

et al. 2018

IJIMAI

View full text Add to dashboard Cite

Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS) and cross diamond search algorithms (CDS) are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS) algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS) in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time.

show abstract

Moving Object Detection Using Background Subtraction

Cited by 52 publications

References 0 publications

Overview of Object Detection and Tracking based on Block Matching Techniques

Overview of Object Detection and Tracking based on Block Matching Techniques

Detection of a Moving Car Based on Invariant Moments

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Contact Info

Product

Resources

About