Comparison of human detection using background subtraction and frame difference

Zaharin, Mohd Saifulnizam; Ibrahim, Norazlin; Dir, Tengku Mohd Azahar Tuan

doi:10.11591/eei.v9i1.1458

Cited by 6 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally in stage three, the XOR-bitwise accumulation and basic (AND and OR) operations are used to decide which pixels are related to the objects and which are not. In terms of the theoretical contribution, the small details (noisy background pixels) should be omitted as in (11):…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Changes in scene and motion detection are two basic steps that play an essential and guiding role in simple and complex environments, where most outdoor surveillance videos are recorded [1]- [6]. However, the variation of the static background in some unfamiliar designs still make the mission of correctly extracting the foreground from the background a widely occurring challenge in surveillance video analysis [7]- [11]. Background subtraction is a necessary task in video applications such as surveillance to track, index, retrieve, and capture the essential metadata of people, cars, and other different moving objects either in real-time or off-time [12]- [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Threshold adaptation and XOR accumulation algorithm for objects detection

Kurmasha

Ali

2022

IJECE

View full text Add to dashboard Cite

<span>Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.</span>

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Threshold adaptation and XOR accumulation algorithm for objects detection

Kurmasha

Ali

2022

IJECE

View full text Add to dashboard Cite

show abstract

“…This method merits high accuracy but demerits to have a static background. Frame Difference: The moving object is identified efficiently at a complex background by taking the difference between the two frames [8,9] but reports with less accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Spatio-Temporal Human Detected Keyframe Extraction

D.,

Priscilla C.

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Due to the immense availability of Closed-Circuit Television surveillance, it is quite difficult for crime investigation due to its huge storage and complex background. Content-based video retrieval is an excellent method to identify the best Keyframes from these surveillance videos. As the crime surveillance reports numerous action scenes, the existing keyframe extraction is not exemplary. At this point, the Spatio-temporal Histogram of Oriented Gradients - Support Vector Machine feature method with the combination of Background Subtraction is appended over the recovered crime video to highlight the human presence in surveillance frames. Additionally, the Visual Geometry Group trains these frames for the classification report of human-detected frames. These detected frames are processed to extract the keyframe by manipulating an inter-frame difference with its threshold value to favor the requisite human-detected keyframes. Thus, the experimental results of HOG-SVM illustrate a compression ratio of 98.54%, which is preferable to the proposed work's compression ratio of 98.71%, which supports the criminal investigation.

show abstract

“…Shang et al [29] propose a three-frame difference for background detection. Zaharin et al [30] established a background subtraction and frame difference model for pedestrian detection. Guo et al [31] dynamically update the background frame and extract the moving object in real-time.…”

Section: Video Information Extractionmentioning

confidence: 99%

A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

Qiu

Cui²,

Meng

2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Video surveillance is an effective way to record current events. In view of the difficulty of efficient transmission of massive surveillance video and the risk of leakage in the transmission process, a new data encryption and fast transmission algorithm is proposed in this paper. From the perspective of events, the constraints of time and space dimension is broken. First, a background and moving object extraction model is built based on video composition. Then, a strong correlation data encryption and fast transmission model is constructed to achieve efficient data compression. Finally, a data mapping mechanism is established to realize the decoding of surveillance video. Our experimental results show that the compression ratio of the proposed algorithm is more than 60% under the premise of image confidentiality.

show abstract

Comparison of human detection using background subtraction and frame difference

Cited by 6 publications

References 14 publications

Threshold adaptation and XOR accumulation algorithm for objects detection

Threshold adaptation and XOR accumulation algorithm for objects detection

An Enhanced Spatio-Temporal Human Detected Keyframe Extraction

A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

Contact Info

Product

Resources

About