Objects In Video Sequences Research Articles

3D-MRF BASED VIDEO TRACKING IN THE COMPRESSED DOMAIN

Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion, drastic illumination change, large pose variation, occlusion, cluttered background and also the camera shake. This paper presents a novel method of object tracking by using the algorithms spatiotemporal Markov random field (STMRF) and online discriminative feature selection (ODFS), which overcome the above mentioned problems and provide a better tracking process. This method is also capable of tracking multiple objects in video sequence even in the presence of an object interactions and occlusions that achieves better results with real time performance.

Motion estimation and segmentation in depth and intensity videos

This paper investigates motion estimation and segmentation of independently moving objects in video sequences that contain depth and intensity information, such as videos captured by a Time of Flight camera. Specifically, we present a motion estimati

A texton-based kernel density estimation approach for background modeling under extreme conditions

Background modeling is a well-know approach to detect moving objects in video sequences. In recent years, background modeling methods that adopt spatial and texture information have been developed for dealing with complex scenarios. However, none of the investigated approaches have been tested under extreme conditions, such as the underwater domain, on which effects compromising the video quality affect negatively the performance of the background modeling process. In order to overcome such difficulties, more significant features and more robust methods must be found. In this paper, we present a kernel density estimation method which models background and foreground by exploiting textons to describe textures within small and low contrasted regions. Comparison with other texture descriptors, namely, local binary pattern (LBP) and scale invariant local ternary pattern (SILTP) shown improved performance. Besides, quantitative and qualitative performance evaluation carried out on three standard datasets showing very complex conditions revealed that our method outperformed state-of-the-art methods that use different features and modeling techniques and, most importantly, it is able to generalize over different scenarios and targets.

Research of AForge.NET in Motion Video Detection

In general, moving target detection method has the background subtraction method,the adjacent frame difference method and the optical flow method. In order to quickly and accurately detect the moving targets of surveillance video , expand the location tracking study.This paper describes the detection method in real-time vehicle moving target,used AForge.NET library and based on the absolute of image gray difference is greater than the taken threshold value. Implementation results show that this method can effectively and quickly detect moving objects in video sequences.

Occlusion and Abandoned Object Detection for Surveillance Applications

Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method. Keyword: Occlusion, Histograms of Oriented Gradients descriptors, Support Vector Machine, mixture of Gaussians techniques, Blob, abandoned object.

Moving Area Filter to Detect Object in Video Sequence from Moving Platform

Moving Area Filter to Detect Object in Video Sequence from Moving Platform

An Emphasizing Approach based on Enhanced Intuitionistic Fuzzy Logic Segmentation on Objects in Video Sequences

paper, a potential moving object modeling suitable for video surveillance correspondence is introduced. Taking into concern the color and motion features of foreground objects in each independent video stream, the proposed method segments the existing moving objects based on the edge detection method and constructs an intuitionistic fuzzy graph based structure to maintain the corresponding information of every segment. Using such graph structures reduces our correspondence problem to a subgraph finest isomorphism problem. The proposed approach is robust against diverse resolutions and orientations of objects at each view. This system uses the Intuitionsitc fuzzy logic to employ a human like color perception in its decision making stage in order to handle color inconstancy. The computational time of the proposed method is made low to be applied in real time applications. It also performs the similarity measure using the intuitionistic fuzzy logic based distance measure for computing the regions relationship. Keywordsadjacency graph, Fuzzy graph, Intuitionistic fuzzy, subgraph, isomorphism.

Background Subtraction Based on Gaussian Mixture Model

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

Frame Interpolation Based on Visual Correspondence and Coherency Sensitive Hashing

The technology of frame interpolation can be applied in intelligent monitoring systems to improve the quality of surveillance video. In this paper, a region-guided frame interpolation algorithm is proposed by introducing two innovative improvements. On the one hand, a detection approach is presented based on visual correspondence for detecting the motion regions that correspond to attracted objects in video sequences, which can narrow the prediction range of interpolated frames. On the other hand, spatial and temporal mapping rules are proposed using coherency sensitive hashing, which can obtain more accurate predicted values of interpolated pixels. Experiments show that the proposed method can achieve encouraging performance in terms of visual quality and quantitative measures.

Pedestrian Detection Based on Active Basis

Pedestrian detection from a video sequence is a challenging problem. In this paper, we mainly use active basis model which consists of a small number of Gabor wavelet elements at selected locations and orientations to detect pedestrian. In order to enhance the detection rate, we propose an adaptive background modeling method for background subtraction method to detect objects in video sequence which emphasize background and remove foreground using mask technology. After acquiring many pedestrian templates of different poses and orientations, active basis method can be applied in pose estimation and human action analysis. The detection results indicate that our approach is capable of obtaining better detecting effects and pose estimation even under conditions of noise and illumination changes.

Moving Object Detection Based on Improved Variational GAC Model

In view of the detection of moving object in video sequence, the traditional moving object detection algorithms are researched. The paper presents a new algorithm for object detection based on initial contour and improved variational GAC model. First, this method built up background model utilizing Gaussian mixture model and background subtraction to extract initial contour of the object; taking initial contour as initial value of curve evolution. Then, an improved restriction item is introduced into variational GAC vector model, the proposed restriction item that is a nonlinear hear equation with normalized diffusion rate, therefore re-initialization procedure of level set function is completely eliminated. Iteration number of curve evolution and run time is reduced. The experimental show that accurate contour of moving object is got and this algorithm is effective and feasible in real video environment.

An Adaptive Neural-Fuzzy Approach for Object Detection in Dynamic Backgrounds for Surveillance Systems

Object detection is a fundamental aspect in surveillance systems. Although several works aimed at detecting objects in video sequences have been reported, many are not tolerant to dynamic background or require complex computation in addition to manual parameter adjustments. This paper proposes an adaptive object detection method to work in dynamic backgrounds without human intervention. The proposed method is based on a neural-fuzzy model. The neural stage, based on a one-to-one self-organizing map (SOM) architecture, deals with the dynamic background for object detection as well as shadow elimination. The fuzzy inference Sugeno system mimics human behavior to automatically adjust the main parameters involved in the SOM detection model, making the system independent of the scenario. Results of the model over real video scenes show its robustness. These findings are comparable to the results obtained with human intervention to define the parameters of the model. A quantitative comparison with methods reported in the literature is also provided to show the performance of the system.

Robust moving shadow detection with hierarchical mixture of MLP experts

Detection and elimination of the shadows of moving objects in video sequences have been one of the major challenges in tracking applications. Since moving shadows cannot be removed from foreground by motion-based background subtraction methods, they lead to confusion and error in moving object tracking. In this paper, a novel classification method based on hierarchical mixture of experts learning for detecting shadows from foreground is proposed. A hierarchical mixture of MLP experts method (HMME) with semi-supervised teacher-directed learning (SSP-HMME) is used. It contains a two-level mixture of experts (ME) system. The main superiority of this method is that it is more robust than state-of-the-art methods in all types of indoor and outdoor environments. The robustness is against the number of light sources, illumination conditions, surface orientations, object sizes, etc., and it is estimated using accuracy rates. The video set has been collected from 7 different datasets. The results of experiments in outdoor and indoor environments show the validity of the method in the improvement on the accuracy of both detection and discrimination rate for moving shadows in video sequences. The results of the experiments show the accuracy rate of 89 % in average in different indoor and outdoor environmental conditions that is about 6 % better than current state-of-the-art methods.

Robust detection of moving objects in video sequences through rough set theory framework

Detection of moving objects in the presence of challenging background situations like swaying vegetation, rippling water, camera jitter etc., is known to be a difficult task. Background subtraction is considered to be better than the other approaches in terms of robustness. Its success primarily depends on the proper choice of background model(s) associated with every pixel for its foreground/background labeling. In this work, we have adopted rough-set theoretic measures to embed the spatial similarity around a neighborhood as a model for the pixel. Basic histon and its associated measure Basic Histon Roughness Index (BHRI) have been reported in the literature. It was applied to still image segmentation with impressive performance. Its adoption in video sequences for foreground/background labeling is proposed herein. We extended the histon concept to a 3D histon, which considers the intensities across the color planes in a combined manner, instead of considering independent color planes. Further, we also incorporated fuzziness into the 3D HRI measure. The labeling decision is based on Bhattacharyya distance between the model HRI and the corresponding measure in the current frame. Adoption of rough set theoretic concept into moving object segmentation is nontrivial, as the model updating requires careful consideration so that the pixels associated with gradually changing background or dynamic background are labeled as background and at the same time, slow moving objects are never adopted into the background model. A novel background model update strategy proposed herein takes these into consideration and also eliminates the need of having exclusive ideal background frame initially.

Region-growing detection of moving objects in video sequences based on optical flow

A region-growing algorithm for detecting moving objects in video sequences based on optical flow is suggested; it calculates the optical flow only for moving objects and does not consider stationary image regions. The algorithm has linear complexity depending on the value of the optical flow window, uses SSE2/SSE3 instructions for significant acceleration of the calculations, and can be used in the real-time mode in problems of detection and tracking of moving objects in video sequences with fragmentary filling of frames with moving objects.

A Robust Method for Moving Object Detection Using Modified Statistical Mean Method

Moving object detection is low-level, important task for any visual surveillance system. One of the aim of this paper is to, to describe various approaches of moving object detection such as background subtraction, temporal difference, as well as pros and cons of these techniques. A statistical mean technique [10] has been used to overcome the problem in previous techniques. Even statistical mean method also suffers with the problem of superfluous effects of foreground objects. In this paper, the presented method tries to overcome this effect as well as reduces the computational complexity up to some extent. In this paper, a robust algorithm for automatic, noise detection and removal from moving objects in video sequences is presented. The algorithm considers static camera parameters.

Exposing Digital Forgeries in Ballistic Motion

We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from video-sharing websites.

A hybrid algorithm for automatic segmentation of slowly moving objects

Segmentation of moving objects in video sequences is a basic task in many applications. However, it is still challenging due to the semantic gap between the low-level visual features and the high-level human interpretation of video semantics. Compared with segmentation of fast moving objects, accurate and perceptually consistent segmentation of slowly moving objects is more difficult. In this paper, a novel hybrid algorithm is proposed for segmentation of slowly moving objects in video sequence aiming to acquire perceptually consistent results. Firstly, the temporal information of the differences among multiple frames is employed to detect initial moving regions. Then, the Gaussian mixture model (GMM) is employed and an improved expectation maximization (EM) algorithm is introduced to segment a spatial image into homogeneous regions. Finally, the results of motion detection and spatial segmentation are fused to extract final moving objects. Experiments are conducted and provide convincing results.

Visual object tracking by an evolutionary self-organizing neural network

In this paper, a recently proposed evolutionary self-organizing map is extended and applied to visual tracking of objects in video sequences. The proposed approach uses a simple geometric template ...

A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking

Sequential Monte Carlo (SMC) represents a principal statistical method for tracking objects in video sequences by on-line estimation of the state of a non-linear dynamic system. The performance of individual stages of the SMC algorithm is usually data-dependent, making the prediction of the performance of a real-time capable system difficult and often leading to grossly overestimated and inefficient system designs. Also, the considerable computational complexity is a major obstacle when implementing SMC methods on purely CPU-based resource constrained embedded systems. In contrast, heterogeneous multi-cores present a more suitable implementation platform. We use hybrid CPU/FPGA systems, as they can efficiently execute both the control-centric sequential as well as the data-parallel parts of an SMC application. However, even with hybrid CPU/FPGA platforms, determining the optimal HW/SW partitioning is challenging in general, and even impossible with a design time approach. Thus, we need self-adaptive architectures and system software layers that are able to react autonomously to varying workloads and changing input data while preserving real-time constraints and area efficiency. In this article, we present a video tracking application modeled on top of a framework for implementing SMC methods on CPU/FPGA-based systems such as modern platform FPGAs. Based on a multithreaded programming model, our framework allows for an easy design space exploration with respect to the HW/SW partitioning. Additionally, the application can adaptively switch between several partitionings during run-time to react to changing input data and performance requirements. Our system utilizes two variants of a add/remove self-adaptation technique for task partitioning inside this framework that achieve soft real-time behavior while trying to minimize the number of active cores. To evaluate its performance and area requirements, we demonstrate the application and the framework on a real-life video tracking case study and show that partial reconfiguration can be effectively and transparently used for realizing adaptive real-time HW/SW systems.