Moving Target Detection and Tracking for Smartphone Automatic Focusing

In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

Real-time detection of moving objects in video sequences

An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.

Efficient Approach for Face Detection in Video Surveillance

Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presented to detect faces in video surveillance.Firstly,both the skin-color and motion components are applied to extract skin-like regions.The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm.Secondly,the image is clustered into separated face candidates by using the region growing technique.Finally,the face candidates are further verified by the rule-based algorithm.Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.

Motion Estimation Based on Global and Local Compensability Analysis

This paper reports our study of a novel motion estimation algorithm based on global and local compensability analysis. The spatial correlation of motion field is used to reduce the burden of estimation computation and extra bit rate for motion vectors. Experimental results show that this algorithm is more efficient than the conventional methods, especially for temporal activity regions.

Video segmentation based on area selection

This paper presents a video motion object segmentation method based on area selection. This method uses a simple and practical space first region segmentation method, it through the motion information and space-time energy model to multiple choice of area, at lask the accurate segmentation object can be obtained throuth some post-processing technology. Experiments prove that this algorithm has good robustness.

Gravitational Energy Levels: Part Two

We present here a model that explains in a simple, easy and summarized manner, the values, meaning and reasons for the force of gravity, using simple physical tools. According to this model, a gravitational field actually creates different energy levels, similar to the atom, around the center of mass of the gravitational source, and a transition between the energy levels results in the creation of the force of weight acting on each small body which is in the gravitational field. As the body approaches a gravitational field, its energy value decreases to a value of m0u(R)2 , proportional to the distance R between the centers of the masses, when u(R) is the magnitude of the self-speed of light vector (the progression in the time axis) of the small body, and its value decreases as it approaches the center of the origin of the field. This change in the energy levels is the cause of the force of gravity. A formula is obtained for the concept of potential gravitational energy and the variables on which it depends, and for the time differences between two frames that are in the gravitational field, taking into account the motion and location of each frame. It is obtained from this model that the speed of light is also a variable value as a result of the effect of the gravitational field.

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive topic.However,one of the main challenges is to effectively extract complementary features from different modalities for action recognition.In this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action recognition.Built on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation features.We evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and PKU-MMD.The results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

Camera-based Basketball Scoring Detection Using Convolutional Neural Network

Recently,deep learning methods have been applied in many real scenarios with the development of convolutional neural networks(CNNs).In this paper,we introduce a camera-based basketball scoring detection(BSD)method with CNN based object detection and frame difference-based motion detection.In the proposed BSD method,the videos of the basketball court are taken as inputs.Afterwards,the real-time object detection,i.e.,you only look once(YOLO)model,is implemented to locate the position of the basketball hoop.Then,the motion detection based on frame difference is utilized to detect whether there is any object motion in the area of the hoop to determine the basketball scoring condition.The proposed BSD method runs in real-time with satisfactory basketball scoring detection accuracy.Our experiments on the collected real scenario basketball court videos show the accuracy of the proposed BSD method.Furthermore,several intelligent basketball analysis systems based on the proposed method have been installed at multiple basketball courts in Beijing,and they provide good performance.

Automatic Segmentation of Moving Objects in Video Sequences for Indoor and Outdoor Applications

Computer vision systems have an impressive spread both for their practicalapplication and for theoretical research . The common approach used in such systems consists of agood segmentation of moving objects from video sequences . This paper presents an automaticalgorithm for segmenting and extracting moving objects suitable for indoor and outdoor videoapplications, where the background scene can be captured beforehand . Since edge detection is oftenused to extract accurate boundaries of the image's objects, the first step in our algorithm isaccomplished by combining two edge maps which are detected from the frame difference in twoconsecutive frames and the background subtraction . After removing edge points that belong to thebackground, the resulting moving edge map is fed to the object extraction step . A fundamental taskin this step is to declare the candidates of the moving object, followed by applying morphologicaloperations. The algorithm is implemented on a real video sequence as well as MPEG- 4 sequence andgood segmentation results are achieved.

Image analyses for video-based remote structure vibration monitoring system

Video-based vibration measurement is a cost-effective way for remote monitoring the health conditions of transportation and other civil structures, especially for situations where accessibility is restricted and does not allow installation of conventional monitoring devices. Besides, video-based system is global measurement. The technical basis of video-based remote vibration measurement system is digital image analysis. Comparison of the images allow the field of motion to be accurately delineated. Such information is important to understand the structure behaviors including the motion and strain distribution. This paper presents system and analyses to monitor the vibration velocity and displacement field. The performance is demonstrated on a testbed of model building. Three different methods （i.e., frame difference method, particle image velocimetry, and optical Flow Method） are utilized to analyze the image sequences to extract the feature of motion. The Performance is validated using accelerometer data. The results indicate that all three methods can estimate the velocity field of the model building, although the results can be affected by factors such as background noise and environmental interference. Optical flow method achieved the best performance among these three methods studied. With further refinement of system hardware and image processing software, it will be developed into a remote video based monitoring system for structural health monitoring of transportation infrastructure to assist the diagnoses of its health conditions.