Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions

3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise

Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects

ROBUST BACKGROUND SUBTRACTION FOR MOVING CAMERAS AND THEIR APPLICATIONS IN EGO-VISION SYSTEMS

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras

Real time pedestrian detection and tracking for driver assistance systems

Real time pedestrian detection and tracking is considered as a critical application. Night time driving is more risky as compared to day time driving because of poor visibility especially in the case of senior citizens. While traditional methods of segmentation using thresholding, background subtraction and background estimation provide satisfactory results to detect single objects, noise is produced in case of multiple objects and in poor lighting conditions. To overcome these difficulties, a new method is proposed for detecting and tracking multiple moving objects on night-time lighting conditions. The method is performed by integrating both the wavelet-based contrast change detector and locally adaptive thresholding scheme. In the initial stage, to detect the potential moving objects contrast in local change over time is used. To suppress false alarms motion prediction and spatial nearest neighbor data association are used. A latest change detector mechanism is implemented to detect the changes in a video sequence and divide the sequence into scenes to be encoded independently. Using the change detector algorithm (CD), it was efficient enough to detect abrupt cuts and help divide the video file into sequences. With this we get a sufficiently good output with less noise. But in some cases noise becomes prominent. Hence, a method called correlation is used which gives the relation between two consecutive frames which have sufficient difference to be used as current and previous frame. This gives a way better result in poor light condition and multiple moving objects

Intelligent surveillance system for street surveillance

CCTV surveillance systems are widely used as a street monitoring tool in public and private areas. This paper presents a novel approach of an intelligent surveillance system that consists of adaptive background modelling, optimal trade-off features tracking and detected moving objects classification. The proposed system is designed to work in real-time. Experimental results show that the proposed background modelling algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background. For the tracking algorithm, the effectiveness between colour, edge and texture features for target and candidate blobs were analysed. Finally, it is also demonstrated that the proposed object classification algorithm performs well with different classes of moving objects such as, cars, motorcycles and pedestrians

Object Tracking: Appearance Modeling And Feature Learning

Object tracking in real scenes is an important problem in computer vision due to increasing usage of tracking systems day in and day out in various applications such as surveillance, security, monitoring and robotic vision. Object tracking is the process of locating objects of interest in every frame of video frames. Many systems have been proposed to address the tracking problem where the major challenges come from handling appearance variation during tracking caused by changing scale, pose, rotation, illumination and occlusion. In this dissertation, we address these challenges by introducing several novel tracking techniques. First, we developed a multiple object tracking system that deals specially with occlusion issues. The system depends on our improved KLT tracker for accurate and robust tracking during partial occlusion. In full occlusion, we applied a Kalman filter to predict the object\u27s new location and connect the trajectory parts. Many tracking methods depend on a rectangle or an ellipse mask to segment and track objects. Typically, using a larger or smaller mask will lead to loss of tracked objects. Second, we present an object tracking system (SegTrack) that deals with partial and full occlusions by employing improved segmentation methods: mixture of Gaussians and a silhouette segmentation algorithm. For re-identification, one or more feature vectors for each tracked object are used after target reappearing. Third, we propose a novel Bayesian Hierarchical Appearance Model (BHAM) for robust object tracking. Our idea is to model the appearance of a target as combination of multiple appearance models, each covering the target appearance changes under a certain situation (e.g. view angle). In addition, we built an object tracking system by integrating BHAM with background subtraction and the KLT tracker for static camera videos. For moving camera videos, we applied BHAM to cluster negative and positive target instances. As tracking accuracy depends mainly on finding good discriminative features to estimate the target location, finally, we propose to learn good features for generic object tracking using online convolutional neural networks (OCNN). In order to learn discriminative and stable features for tracking, we propose a novel object function to train OCNN by penalizing the feature variations in consecutive frames, and the tracker is built by integrating OCNN with a color-based multi-appearance model. Our experimental results on real-world videos show that our tracking systems have superior performance when compared with several state-of-the-art trackers. In the feature, we plan to apply the Bayesian Hierarchical Appearance Model (BHAM) for multiple objects tracking

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Galaxy clusters identified from the SDSS DR6 and their properties

Clusters of galaxies in most previous catalogs have redshifts z<0.3. Using the photometric redshifts of galaxies from the Sloan Digital Sky Survey Data Release 6 (SDSS DR6), we identify 39,668 clusters in the redshift range 0.05< z <0.6 with more than eight luminous (M_r<-21) member galaxies. Cluster redshifts are estimated accurately with an uncertainty less than 0.022. The contamination rate of member galaxies is found to be roughly 20%, and the completeness of member galaxy detection reaches to ~90%. Monte Carlo simulations show that the cluster detection rate is more than 90% for massive (M_{200}>2\times10^{14} M_{\odot}) clusters of z<0.42. The false detection rate is ~5%. We obtain the richness, the summed luminosity, and the gross galaxy number within the determined radius for identified clusters. They are tightly related to the X-ray luminosity and temperature of clusters. Cluster mass is related to the richness and summed luminosity with M_{200}\propto R^{1.90\pm0.04} and M_{200}\propto L_r^{1.64\pm0.03}, respectively. In addition, 685 new candidates of X-ray clusters are found by cross-identification of our clusters with the source list of the ROSAT X-ray survey.Comment: 18 pages, 27 figures, 2 tables. Further modefication on Table 1 and Table 2 after formal publication on ApJS. No changes on conclusions except for the number of clusters we found. The updated tables are available at "http://159.226.88.6/zmtt/wzl/CV_wen.htm