Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Efficient Video Transport over Lossy Networks

Nowadays, packet video is an important application of the Internet. Unfortunately the capacity of the Internet is still very heterogeneous because it connects high bandwidth ATM networks as well as low bandwidth ISDN dial in lines. The MPEG-2 and MPEG-4 video compression standards provide efficient video encoding for high and low bandwidth media streams. In particular they include two paradigms which make those standards suitable for the transmission of video via heterogeneous networks. Both support layered video streams and MPEG-4 additionally allows the independent coding of video objects. In this paper we discuss those two paradigms, give an overview of the MPEG video compression standards and describe transport protocols for Real Time Media transport over lossy networks. Furthermore, we propose a real-time segmentation approach for extracting video objects in teleteaching scenarios

Foreground detection of video through the integration of novel multiple detection algorithims

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe main outcomes of this research are the design of a foreground detection algorithm, which is more accurate and less time consuming than existing algorithms. By the term accuracy we mean an exact mask (which satisfies the respective ground truth value) of the foreground object(s). Motion detection being the prior component of foreground detection process can be achieved via pixel based and block based methods, both of which have their own merits and disadvantages. Pixel based methods are efficient in terms of accuracy but a time consuming process, so cannot be recommended for real time applications. On the other hand block based motion estimation has relatively less accuracy but consumes less time and is thus ideal for real-time applications. In the first proposed algorithm, block based motion estimation technique is opted for timely execution. To overcome the issue of accuracy another morphological based technique was adopted called opening-and-closing by reconstruction, which is a pixel based operation so produces higher accuracy and requires lesser time in execution. Morphological operation opening-and-closing by reconstruction finds the maxima and minima inside the foreground object(s). Thus this novel simultaneous process compensates for the lower accuracy of block based motion estimation. To verify the efficiency of this algorithm a complex video consisting of multiple colours, and fast and slow motions at various places was selected. Based on 11 different performance measures the proposed algorithm achieved an average accuracy of more than 24.73% than four of the well-established algorithms. Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in this next proposed algorithm. For the said purpose the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results

Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement

We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods

Utilization of Robust Video Processing Techniques to Aid Efficient Object Detection and Tracking

AbstractIn this research, data acquired by Unmanned Aerial Vehicles (UAVs) are primarily used to detect and track moving objects which pose a major security threat along the United States southern border. Factors such as camera motion, poor illumination and noise make the detection and tracking of moving objects in surveillance videos a formidable task. The main objective of this research is to provide a less ambiguous image data for object detection and tracking by means of noise reduction, image enhancement, video stabilization, and illumination restoration. The improved data is later utilized to detect and track moving objects in surveillance videos. An optimization based image enhancement scheme was successfully implemented to increase edge information to facilitate object detection. Noise present in the raw video captured by the UAV was efficiently removed using search and match methodology. Undesired motion induced in the video frames was eliminated using block matching technique. Moving objects were detected and tracked by using contour information resulting from the implementation of adaptive background subtraction based detection process. Our simulation results shows the efficiency of these algorithms in processing noisy, un-stabilized raw video sequences which were utilized to detect and track moving objects in the video sequences