An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks

Chen, Bo‐Hao

doi:10.1109/tmm.2014.2298377

Cited by 70 publications

(20 citation statements)

References 19 publications

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“…So the classifier performs one by one the PCA from the positive examples and from the negative examples; once it has to classify a brand new pattern it comes it into each sets of PCs and compares the reconstructions, assignment the instance to the category with the smallest reconstruction error. The system is ready to observe frontal and rear views of pedestrians, and frequently may detect side views of pedestrians despite not being trained for this task [1][2][3].…”

Section: Pca Methodsmentioning

confidence: 99%

Detection and Monitoring of Real - time Moving Object using PCA with Window Sliding

Mardikar¹,

Futane²

2016

IJERT

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Moving object detection and tracking is commonly the primary step in applications like video surveillance. The main aim of project is moving object detection and tracking system with a static camera has been developed to estimate distance parameters propose a general moving object detection and tracking supported vision system using Principle component analysis and window sliding algorithm. This paper focuses on detection of moving objects in a very scene for instance moving people meeting one another, and tracking and detected objects as long as they stay within the scene. This can be done by Principle component analysis and window sliding algorithm with software and that could calculate distance, frame per time. This paper describes an algorithm to estimate moving object using image processing technique from the camera calibration parameter.

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Section: Pca Methodsmentioning

confidence: 99%

Detection and Monitoring of Real - time Moving Object using PCA with Window Sliding

Mardikar¹,

Futane²

2016

IJERT

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“…Huang et al [31] have presented a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. Chen et al [32] have presented an approach that is applicable not only in high bit-rate video streams but also in low bit-rate video streams. Cao et al [33] have presented a unified framework for addressing the difficulties of dynamic background and irregular object movement, especially the one caused by irregular object movement.…”

Section: Related Workmentioning

confidence: 99%

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Chen

et al. 2016

Sensors

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During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder.

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“…At present, object detection algorithms based on deep learning tend to be relatively mature and can have better detection performance in specific scenes, such as pedestrian detection [ 7 ], face detection [ 8 ], etc. These algorithms can be widely used in intelligent monitoring systems [ 9 ], intelligent transportation systems [ 10 ], military object detection [ 11 ], medical object detection [ 12 ], etc. However, there is more room for optimization in more special scenes [ 13 ], such as the problems of occlusion, too small scale, deformation, and camouflage of the object in the image.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information

Xiao

Wang

Zhang

et al. 2020

Sensors

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Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.

show abstract

An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks

Cited by 70 publications

References 19 publications

Detection and Monitoring of Real - time Moving Object using PCA with Window Sliding

Detection and Monitoring of Real - time Moving Object using PCA with Window Sliding

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information

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