Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain

In this paper, we show that we can apply probabilistic spatiotemporal macroblock filtering (PSMF) and partial decoding processes to effectively detect and track multiple objects in real time in H.264|AVC bitstreams with stationary background. Our contribution is that our method cannot only show fast processing time but also handle multiple moving objects that are articulated, changing in size or internally have monotonous color, even though they contain a chaotic set of non-homogeneous motion vectors inside. In addition, our partial decoding process for H.264|AVC bitstreams enables to improve the accuracy of object trajectories and overcome long occlusion by using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200

Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features. This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases. Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Object detection and activity recognition in digital image and video libraries

This thesis is a comprehensive study of object-based image and video retrieval, specifically for car and human detection and activity recognition purposes. The thesis focuses on the problem of connecting low level features to high level semantics by developing relational object and activity presentations. With the rapid growth of multimedia information in forms of digital image and video libraries, there is an increasing need for intelligent database management tools. The traditional text based query systems based on manual annotation process are impractical for today\u27s large libraries requiring an efficient information retrieval system. For this purpose, a hierarchical information retrieval system is proposed where shape, color and motion characteristics of objects of interest are captured in compressed and uncompressed domains. The proposed retrieval method provides object detection and activity recognition at different resolution levels from low complexity to low false rates. The thesis first examines extraction of low level features from images and videos using intensity, color and motion of pixels and blocks. Local consistency based on these features and geometrical characteristics of the regions is used to group object parts. The problem of managing the segmentation process is solved by a new approach that uses object based knowledge in order to group the regions according to a global consistency. A new model-based segmentation algorithm is introduced that uses a feedback from relational representation of the object. The selected unary and binary attributes are further extended for application specific algorithms. Object detection is achieved by matching the relational graphs of objects with the reference model. The major advantages of the algorithm can be summarized as improving the object extraction by reducing the dependence on the low level segmentation process and combining the boundary and region properties. The thesis then addresses the problem of object detection and activity recognition in compressed domain in order to reduce computational complexity. New algorithms for object detection and activity recognition in JPEG images and MPEG videos are developed. It is shown that significant information can be obtained from the compressed domain in order to connect to high level semantics. Since our aim is to retrieve information from images and videos compressed using standard algorithms such as JPEG and MPEG, our approach differentiates from previous compressed domain object detection techniques where the compression algorithms are governed by characteristics of object of interest to be retrieved. An algorithm is developed using the principal component analysis of MPEG motion vectors to detect the human activities; namely, walking, running, and kicking. Object detection in JPEG compressed still images and MPEG I frames is achieved by using DC-DCT coefficients of the luminance and chrominance values in the graph based object detection algorithm. The thesis finally addresses the problem of object detection in lower resolution and monochrome images. Specifically, it is demonstrated that the structural information of human silhouettes can be captured from AC-DCT coefficients

Content Based Retrieval Using Colour And Texture Of Wavelet Based Compressed Images [TA1637. I67 2008 f rb].

Permintaan yang tinggi terhadap penggunaan dapatan semula imej telah menggalakkan pembangun aplikasi multimedia untuk mencari cara untuk mengurus dan mencari imej dengan lebih efisien. The growing demands for image retrieval in multimedia field such as crime prevention, health informatics and biometrics has pushed application developers to search ways to manage and retrieve images more efficiently

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival