Pedestrian-Flow Statistics Algorithm Based on Image Processing

2013 ◽  
Vol 347-350 ◽  
pp. 3381-3384
Author(s):  
Hong Mei Jian ◽  
Zheng Xi Wei
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Target Detection ◽  
Mean Shift ◽  
Research Field ◽  
Experimental Results ◽  
Pedestrian Flow ◽  
Coverage Problem ◽  
Mean Shift Algorithm ◽  
Svm Model

The automatic statistics to pedestrian flow can greatly improve the duty staff efficiency and thus becomes the hot research field. This paper presents a pedestrian flow statistics algorithm based on image-processing. Our research is on the basis of color histogram and mean-shift algorithm, and introduces the SVM model through generating confidence function to judge the actual attribute of background pixels. Such way improves the accuracy of target detection. Experimental results prove our algorithm better handles to pedestrian's coverage problem and meets the requirement of real-time and accuracy.

The Improvement of Mean - Shift Algorithm in the Video of Global Visual Robotic Fish in Tracking Moving Targets

2013 ◽  
Vol 475-476 ◽  
pp. 947-951
Author(s):  
Zhi Yuan Mai ◽  
Kun Yu Tan ◽  
An Ting Xu ◽  
Wei Xiang
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Robotic Fish ◽  
Experimental Results ◽  
Moving Targets ◽  
Mean Shift Algorithm ◽  
Good For ◽  
The Difference

The tracking effect is not good for the faster track with Mean Shift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish.To solve the difficulty of tracking drastically moving targets in this paper, determining the position of moving targets in the next frame through comparing with two bc coefficients which have been set when the Epanechnikov has been selected core to estimate is indeed. The experimental results show the proposed algorithm can track the moving targets efficiently and precisely in video,and also can meet high real-time situation with small calculation.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Automatic Acoustic Target Detecting and Tracking on the Azimuth Recording Diagram with Image Processing Methods

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5391
Author(s):  
Fan Yin ◽  
Chao Li ◽  
Haibin Wang ◽  
Fan Yang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Target Detection ◽  
Ambient Noise ◽  
Arrival Direction ◽  
Processing Methods ◽  
Acoustic Target ◽  
Real Time Tracking ◽  
Passive Acoustic ◽  
Target Detecting

Passive acoustic target detection has been a hot research topic for a few decades. Azimuth recording diagram is one of the most promising techniques to estimate the arrival direction of the interested signal by visualizing the sound wave information. However, this method is challenged by the random ambient noise, resulting in low reliability and short effective distance. This paper presents a real-time postprocessing framework for passive acoustic target detection modalities by using a sonar array, in which image processing methods are used to automate the target detecting and tracking on the azimuth recording diagram. The simulation results demonstrate that the proposed approach can provide a higher reliability compared with the conventional ones, and is suitable for the constraints of real-time tracking.

Target Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

The Development of Image Processing Board Based on Target Tracking

2013 ◽  
Vol 411-414 ◽  
pp. 1322-1325
Author(s):  
Ya Hui Hu ◽  
Le Jiang Guo ◽  
Xiao Lei ◽  
Cheng Min
Keyword(s):  
Image Processing ◽  
Target Tracking ◽  
Internal Structure ◽  
Development Process ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Algorithm Analysis ◽  
Mean Shift Algorithm ◽  
Specific Target ◽  
Target Environment

This paper selects the target tracking algorithm suitable for specific target environment: using Mean Shift algorithm based on space edge direction histogram at initialization, selecting tracking algorithm based on block when there is a shelter. On the basis of algorithm analysis and software experiment and studying of TI Company's TMS320DM642 DSP chip internal structure and development process, these two algorithms researched in this paper were transplanted to DSP platform and a series of optimization were been made to the algorithms codes after transplanted ,implementing target tracking and identified via DSP development board instead of PC.

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 1584-1589
Author(s):  
Zhen Hui Xu ◽  
Bao Quan Mao ◽  
Li Xu ◽  
Jun Yan Zhao
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Kalman Filtering ◽  
Mean Shift ◽  
Moving Target ◽  
Tracking Method ◽  
The Real ◽  
Mean Shift Algorithm ◽  
Compensation Function ◽  
Tracking Technology

In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.

scholarly journals Multimodal verge for scale and pose variant real time face tracking and recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 665
Author(s):  
Ramkumar Govindaraj ◽  
E. Logashanmugam
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Real Time ◽  
Research Field ◽  
Face Tracking ◽  
Low Rank ◽  
Facial Features ◽  
The Face ◽  
Low Rank Representation ◽  
Tracking And Recognition

In recent times face tracking and face recognition have turned out to be increasingly dynamic research field in image processing. This work proposed the framework DEtecting Contiguous Outliers in the LOw-rank Representation for face tracking, in this algorithm the background is assessed by a low-rank network and foreground articles can be distinguished as anomalies. This is suitable for non-rigid foreground motion and moving camera. The face of a foreground person is caught from the frame and then it is contrasted and the speculated pictures stored in the dataset. Here we used Viola-Jones algorithm for face recognition. This approach outperforms the traditional algorithms on multimodal video methodologies and it works adequately on extensive variety of security and surveillance purposes. Results on the continuous demonstrate that the proposed calculation can correctly obtain facial features points. The algorithm is relegate on the continuous camera input and under ongoing ecological conditions.

scholarly journals A High-Precision, Real-Time, and Robust Indoor Visible Light Positioning Method Based on Mean Shift Algorithm and Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Light Emitting Diode ◽  
Mean Shift ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Mean Shift Algorithm

Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.

A Knowledge-Based Real-Time Vision System for Monitoring the Inside of a Fluid Bed Heat Exchanger Chamber

2018 ◽  
Vol 28 (09) ◽  
pp. 1249-1265
Author(s):  
HyungJun Kim
Keyword(s):  
Image Processing ◽  
Heat Exchanger ◽  
Real Time ◽  
Vision System ◽  
Thermal Power ◽  
Experimental Results ◽  
Image Analysis System ◽  
Real Time Monitoring ◽  
Knowledge Based ◽  
Fluid Bed

In this paper, we present a vision system with a special camera for knowledge-based real-time monitoring of the inside of a fluid bed heat exchanger (FBHE) chamber in a thermal power plant. With the proposed system, it is possible to monitor the internal flux condition and analyze the inner temperature of a chamber. Due to the fact that the mixture of coal and sand inside a chamber flows by very quickly, there is an immense amount of smoke and dust, which make it difficult to capture images and analyze an existing system. An adaptive average method is proposed here to observe the background internal environment of an FBHE chamber. The experimental results show that real-time monitoring is possible, even under hot and dusty conditions. Preliminary experimental results confirm expectations about the possibility and effectiveness of the developed device for commercialized real-time monitoring systems. They demonstrate that a single camera with embedded image processing software can concurrently analyze the degree of fluidization of a mixture and the temperature of the chamber inside, even in extremely harsh and hazardous conditions. We aim to eventually develop an image analysis system that combines image processing and knowledge engineering techniques.

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