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Paisitkriangkrai S.,University of Adelaide | Mei T.,Microsoft | Zhang J.,University of New South Wales | Zhang J.,Neville Roach Laboratory | Hua X.-S.,Microsoft
International Journal of Computer Mathematics | Year: 2011

Searching for near-duplicate content has become an important task in many multimedia applications, for example, images, videos and music. The ability to detect duplicate videos plays an important role in several video applications, for example, effective video search, copyright infringement and the study on users' behaviour on near-duplicate video production. Current web video search systems rely only on text keywords and, hence, fail to detect many duplicate videos. In this paper, we analyse the problem of near-duplicate detection and propose a practical solution for real-time large-scale video retrieval. Unlike many existing approaches which make use of video frames or key-frames, our solution is based on a more discriminative signature of video clips. The feature used in this paper is an extension of ordinal measures which have proven to be robust to change in brightness, compression formats and compression ratios. For efficient retrieval, we propose to use multi-probe locality sensitive hashing (MPLSH) to index the video clips for fast similarity search and high recall. MPLSH is able to filter out a large number of dissimilar clips from video database. To refine the search process, we apply a similarity voting based on video clip signatures. Experimental results on the dataset of 12,790 web videos show that the proposed approach improves average precision over the baseline colour histogram approach while satisfying real-time requirements. © 2011 Copyright Taylor and Francis Group, LLC.

Kusakunniran W.,University of New South Wales | Kusakunniran W.,Neville Roach Laboratory | Wu Q.,University of Technology, Sydney | Zhang J.,Neville Roach Laboratory | And 5 more authors.
IEEE Transactions on Information Forensics and Security | Year: 2013

Human gait is an important biometric feature which is able to identify a person remotely. However, change of view causes significant difficulties for recognizing gaits. This paper proposes a new framework to construct a new view-invariant feature for cross-view gait recognition. Our view-normalization process is performed in the input layer (i.e., on gait silhouettes) to normalize gaits from arbitrary views. That is, each sequence of gait silhouettes recorded from a certain view is transformed onto the common canonical view by using corresponding domain transformation obtained through invariant low-rank textures (TILTs). Then, an improved scheme of procrustes shape analysis (PSA) is proposed and applied on a sequence of the normalized gait silhouettes to extract a novel view-invariant gait feature based on procrustes mean shape (PMS) and consecutively measure a gait similarity based on procrustes distance (PD). Comprehensive experiments were carried out on widely adopted gait databases. It has been shown that the performance of the proposed method is promising when compared with other existing methods in the literature. © 2013 IEEE.

Kusakunniran W.,Mahidol University | Wu Q.,University of Technology, Sydney | Zhang J.,University of Technology, Sydney | Zhang J.,Neville Roach Laboratory | And 3 more authors.
IEEE Transactions on Image Processing | Year: 2014

Human gait is an important biometric feature, which can be used to identify a person remotely. However, view change can cause significant difficulties for gait recognition because it will alter available visual features for matching substantially. Moreover, it is observed that different parts of gait will be affected differently by view change. By exploring relations between two gaits from two different views, it is also observed that a part of gait in one view is more related to a typical part than any other parts of gait in another view. A new method proposed in this paper considers such variance of correlations between gaits across views that is not explicitly analyzed in the other existing methods. In our method, a novel motion co-clustering is carried out to partition the most related parts of gaits from different views into the same group. In this way, relationships between gaits from different views will be more precisely described based on multiple groups of the motion co-clustering instead of a single correlation descriptor. Inside each group, a linear correlation between gait information across views is further maximized through canonical correlation analysis (CCA). Consequently, gait information in one view can be projected onto another view through a linear approximation under the trained CCA subspaces. In the end, a similarity between gaits originally recorded from different views can be measured under the approximately same view. Comprehensive experiments based on widely adopted gait databases have shown that our method outperforms the state-of-the-art. © 2013 IEEE.

Kusakunniran W.,University of New South Wales | Kusakunniran W.,Neville Roach Laboratory | Wu Q.,University of Technology, Sydney | Zhang J.,University of New South Wales | And 3 more authors.
IEEE Transactions on Circuits and Systems for Video Technology | Year: 2012

It is well recognized that gait is an important biometric feature to identify a person at a distance, e.g., in video surveillance application. However, in reality, change of viewing angle causes significant challenge for gait recognition. A novel approach using regression-based view transformation model (VTM) is proposed to address this challenge. Gait features from across views can be normalized into a common view using learned VTM(s). In principle, a VTM is used to transform gait feature from one viewing angle (source) into another viewing angle (target). It consists of multiple regression processes to explore correlated walking motions, which are encoded in gait features, between source and target views. In the learning processes, sparse regression based on the elastic net is adopted as the regression function, which is free from the problem of overfitting and results in more stable regression models for VTM construction. Based on widely adopted gait database, experimental results show that the proposed method significantly improves upon existing VTM-based methods and outperforms most other baseline methods reported in the literature. Several practical scenarios of applying the proposed method for gait recognition under various views are also discussed in this paper. © 2012 IEEE.

Kusakunniran W.,University of New South Wales | Wu Q.,University of Technology, Sydney | Zhang J.,Neville Roach Laboratory | Zhang J.,University of Technology, Sydney | And 2 more authors.
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2012

Gait has been known as an effective biometric feature to identify a person at a distance. However, variation of walking speeds may lead to significant changes to human walking patterns. It causes many difficulties for gait recognition. A comprehensive analysis has been carried out in this paper to identify such effects. Based on the analysis, Procrustes shape analysis is adopted for gait signature description and relevant similarity measurement. To tackle the challenges raised by speed change, this paper proposes a higher order shape configuration for gait shape description, which deliberately conserves discriminative information in the gait signatures and is still able to tolerate the varying walking speed. Instead of simply measuring the similarity between two gaits by treating them as two unified objects, a differential composition model (DCM) is constructed. The DCM differentiates the different effects caused by walking speed changes on various human body parts. In the meantime, it also balances well the different discriminabilities of each body part on the overall gait similarity measurements. In this model, the Fisher discriminant ratio is adopted to calculate weights for each body part. Comprehensive experiments based on widely adopted gait databases demonstrate that our proposed method is efficient for cross-speed gait recognition and outperforms other state-of-the-art methods. © 1996-2012 IEEE.

Cai C.,Neville Roach Laboratory | Cai C.,University of New South Wales | Wang Y.,Neville Roach Laboratory | Wang Y.,University of New South Wales | And 2 more authors.
IET Intelligent Transport Systems | Year: 2013

This study presents a method that combines travel-time estimation and adaptive traffic signal control. The proposed method explores the concept of vehicle-to-infrastructure communication, through which real-time vehicle localisation data become available to traffic controllers. This provides opportunity to frequently sample vehicle location and speed for online travel-time estimation. The control objective is to minimise travel time for vehicles in the system. The proposed method is based on approximate dynamic programming, which allows the controller to learn from its own performance progressively. The authors use micro-traffic simulation to evaluate the control performance against benchmark control methods in an idealistic environment, where errors in sampling vehicle location and speed are not considered. The results show that the proposed method outperforms benchmarking methods substantially and consistently. © The Institution of Engineering and Technology 2013.

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