REAL-TIME OBJECT DETECTION USING TWO BACKGROUND MODELS UNDER SHAKING CAMERA

2009 ◽  
Vol 06 (01) ◽  
pp. 13-21 ◽  
Author(s):  
TAEHO KIM ◽  
KANG-HYUN JO
Keyword(s):  
Moving Objects ◽  
Displacement Vector ◽  
Motion Vector ◽  
Computational Cost ◽  
Image Sequence ◽  
Background Model ◽  
Experimental Result ◽  
Camera Motion ◽  
Short Period ◽  
Background Models

In this paper, we propose a novel approach to detect moving objects by two background models, multiple background model (MBM) and temporal median background (TMB), from hand-taken image sequence. For this purpose, we record image sequences by hand-held camera without tripod so every frame has variation between consecutive frames. A pixel-based background model is fragile while image sequence has variation. Therefore we calculate the camera movement using correlation between two consecutive images and it helps us to generate MBM under shaking camera. The computational cost of correlation quickly increases if image resolution increases. Hence, we use edge segments to reduce computational cost. These edge segments are gathered by Sobel operator and those are distinctive spatial features to calculate similarity between two regions, belonging to current and previous images, organized by neighbors of edge segments. Based on the similarity result, we obtain a set of best matched regions, centroids of matched regions, and displacement vectors from each pair of previous and current images. Each displacement vector in a set describes the transition of each matched region in the image pair. Using the highest density of displacement vector histogram, we choose the camera motion vector, indicates camera movement between consecutive frames. According to the camera motion vector, every pixel in a current image is related to different position pixels in a previous image. The pixel relation is used to generate MBM in this paper, unlike original MBM [Xiao, M., Han, C. and Kang, K. [2006]. Proc. Int. Conf. Information Fuscon, pp. 1–7.]. The MBM algorithm classifies the variation of pixel values in frame sequence to several clusters. Classification of varying pixel values to several clusters is similar with mixture of gaussian (MOG). Nevertheless, MBM has low cost to calculate because it does not need to estimate parameter. However, MBM is not sensitive to short period changes. Therefore, we use TMB to support MBM. The experimental result shows that proposed algorithm successfully detects moving objects using background subtraction less than 25 ms per frame when camera has 2D translation.

DETECTION OF MOVING OBJECT USING REMAINED BACKGROUND UNDER MOVING CAMERA

2007 ◽  
Vol 04 (03) ◽  
pp. 227-236 ◽  
Author(s):  
TAEHO KIM ◽  
KANG-HYUN JO
Keyword(s):  
Correlation Coefficient ◽  
Moving Objects ◽  
Displacement Vector ◽  
Image Sequence ◽  
Moving Object ◽  
Pixel Intensity ◽  
Current Image ◽  
Moving Camera ◽  
Lower Weight ◽  
Displacement Vectors

A background is a part that does not vary too much or change frequently in an image sequence. Using this assumption, an algorithm of reconstructing remained background and detecting moving objects for static and also moving camera is presented. For generating background, we detect regions that have high correlation coefficient compared within prior pyramid images from the current image. These detected regions are used for two process. First, we calculate the temporal displacement vector of each detected regions and classify clusters of pixel intensity based on camera movement. Second, we calculate temporally principal displacement vector using histogram of displacement vectors. Temporally principal displacement vector indicates camera movement. Finally we eliminate clusters which have lower weight than threshold, and combine remained clusters for each pixel to generate multiple background clusters. Experimental results show that remained background model and detected moving object under camera moving.

Background Modeling Approach Based on Eigenspace Decomposition

2012 ◽  
Vol 157-158 ◽  
pp. 1399-1403
Author(s):  
Jian Wu Long ◽  
Xuan Jing Shen ◽  
Hai Peng Chen
Keyword(s):  
Video Sequence ◽  
Moving Objects ◽  
Principal Component ◽  
Background Model ◽  
Training Set ◽  
Training Samples ◽  
The Matrix ◽  
Eigenspace Decomposition ◽  
Svd Decomposition ◽  
Background Models

In this work principal component analysis (PCA) was adopted to construct a background model and moving objects were detected by background subtraction method. Firstly, constructed the matrix of training samples by means of converting the video sequence to vectors. Then calculated the covariance matrix C of the training set, and acquired the eigenvalues and eigenvectors of C through SVD decomposition. Next, sorted the eigenvalues and reconstructed the background model by using several image vectors which had higher cumulative contribution. Finally, comparison experiments are performed with the detection results by GMM approach. Experimental results show that the proposed method in this paper could establish background models more accurate and have better effective of object detection.

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

2018 ◽  
pp. 177-192
Author(s):  
Raviraj Pandian ◽  
Ramya A.
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Graph Cut ◽  
Background Model ◽  
Camera Motion ◽  
Current Frame ◽  
Outdoor Environments ◽  
Previous Frame ◽  
Indoor And Outdoor Environments ◽  
Graph Cut Segmentation

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

scholarly journals Mechanical and Electronic Video Stabilization Strategy of Mortars with Trajectory Correction Fuze Based on Infrared Image Sensor

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2461 ◽  
Author(s):  
Cong Zhang ◽  
Dongguang Li
Keyword(s):  
Motion Vector ◽  
Computational Cost ◽  
Infrared Image ◽  
Image Sensor ◽  
Projection Algorithm ◽  
Video Stabilization ◽  
Sensors And Actuators ◽  
Simulation And Experiment ◽  
Transformation Relation ◽  
Trajectory Correction

For a higher attack accuracy of projectiles, a novel mechanical and electronic video stabilization strategy is proposed for trajectory correction fuze. In this design, the complexity of sensors and actuators were reduced. To cope with complex combat environments, an infrared image sensor was used to provide video output. Following the introduction of the fuze’s workflow, the limitation of sensors for mechanical video stabilization on fuze was proposed. Particularly, the parameters of the infrared image sensor that strapdown with fuze were calculated. Then, the transformation relation between the projectile’s motion and the shaky video was investigated so that the electronic video stabilization method could be determined. Correspondingly, a novel method of dividing sub-blocks by adaptive global gray threshold was proposed for the image pre-processing. In addition, the gray projection algorithm was used to estimate the global motion vector by calculating the correlation between the curves of the adjacent frames. An example simulation and experiment were implemented to verify the effectiveness of this strategy. The results illustrated that the proposed algorithm significantly reduced the computational cost without affecting the accuracy of the motion estimation. This research provides theoretical and experimental basis for the intelligent application of sensor systems on fuze.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

scholarly journals The determination of high-resolution spatio-temporal glacier motion fields from time-lapse sequences

2017 ◽  
Vol 5 (4) ◽  
pp. 861-879 ◽  
Author(s):  
Ellen Schwalbe ◽  
Hans-Gerd Maas
Keyword(s):  
High Resolution ◽  
Vector Fields ◽  
Motion Vector ◽  
Image Sequence ◽  
Image Sequences ◽  
Glacier Surface ◽  
Sequence Processing ◽  
Image Sequence Processing ◽  
Matching Techniques

Abstract. This paper presents a comprehensive method for the determination of glacier surface motion vector fields at high spatial and temporal resolution. These vector fields can be derived from monocular terrestrial camera image sequences and are a valuable data source for glaciological analysis of the motion behaviour of glaciers. The measurement concepts for the acquisition of image sequences are presented, and an automated monoscopic image sequence processing chain is developed. Motion vector fields can be derived with high precision by applying automatic subpixel-accuracy image matching techniques on grey value patterns in the image sequences. Well-established matching techniques have been adapted to the special characteristics of the glacier data in order to achieve high reliability in automatic image sequence processing, including the handling of moving shadows as well as motion effects induced by small instabilities in the camera set-up. Suitable geo-referencing techniques were developed to transform image measurements into a reference coordinate system.The result of monoscopic image sequence analysis is a dense raster of glacier surface point trajectories for each image sequence. Each translation vector component in these trajectories can be determined with an accuracy of a few centimetres for points at a distance of several kilometres from the camera. Extensive practical validation experiments have shown that motion vector and trajectory fields derived from monocular image sequences can be used for the determination of high-resolution velocity fields of glaciers, including the analysis of tidal effects on glacier movement, the investigation of a glacier's motion behaviour during calving events, the determination of the position and migration of the grounding line and the detection of subglacial channels during glacier lake outburst floods.

Are Social Background Models Time-Bound?

1986 ◽  
Vol 80 (3) ◽  
pp. 957-967 ◽  
Author(s):  
S. Sidney Ulmer
Keyword(s):  
Supreme Court ◽  
Social Background ◽  
Research Note ◽  
Background Model ◽  
Confounding Variable ◽  
Background Models

In this research note I seek to determine whether a significantly predicting social background model for analyzing the votes of Supreme Court justices is time-bound. I argue that an affirmative result poses serious questions for past uses of such models, none of which has controlled for the possibility that time is a confounding variable. A model that significantly predicted the votes of the justices in the Court's 1903–1968 terms was constructed. Analysis with this model for two periods—from 1903 to 1935, and from 1936 to 1968—established that the model was not timeneutral. Appropriate theoretical implications are drawn.

scholarly journals Crack Propagation Velocity Determination by High-speed Camera Image Sequence Processing

2020 ◽  
Author(s):  
Frank Liebold ◽  
Ali A. Heravi ◽  
Oliver Mosig ◽  
Manfred Curbach ◽  
Viktor Mechtcherine ◽  
...  
Keyword(s):  
Crack Propagation ◽  
High Speed ◽  
Vector Fields ◽  
Displacement Vector ◽  
Image Sequence ◽  
High Speed Camera ◽  
Sequence Processing ◽  
Image Sequence Processing ◽  
Propagation Velocities

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to established techniques is in the fact of its allowing for spatio-temporally resolved, full-field measurements rather than point-wise measurements and that information on crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying to these datasets the image sequence processing procedure as developed, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

Sign in / Sign up

Export Citation Format

Share Document