1,527 research outputs found
Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments
Exploiting Multiple Detections for Person Re-Identification
Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based metho
Person re-Identification over distributed spaces and time
PhDReplicating the human visual system and cognitive abilities that the brain uses to process the
information it receives is an area of substantial scientific interest. With the prevalence of video
surveillance cameras a portion of this scientific drive has been into providing useful automated
counterparts to human operators. A prominent task in visual surveillance is that of matching
people between disjoint camera views, or re-identification. This allows operators to locate people
of interest, to track people across cameras and can be used as a precursory step to multi-camera
activity analysis. However, due to the contrasting conditions between camera views and their
effects on the appearance of people re-identification is a non-trivial task. This thesis proposes
solutions for reducing the visual ambiguity in observations of people between camera views
This thesis first looks at a method for mitigating the effects on the appearance of people under
differing lighting conditions between camera views. This thesis builds on work modelling
inter-camera illumination based on known pairs of images. A Cumulative Brightness Transfer
Function (CBTF) is proposed to estimate the mapping of colour brightness values based on limited
training samples. Unlike previous methods that use a mean-based representation for a set of
training samples, the cumulative nature of the CBTF retains colour information from underrepresented
samples in the training set. Additionally, the bi-directionality of the mapping function
is explored to try and maximise re-identification accuracy by ensuring samples are accurately
mapped between cameras.
Secondly, an extension is proposed to the CBTF framework that addresses the issue of changing
lighting conditions within a single camera. As the CBTF requires manually labelled training
samples it is limited to static lighting conditions and is less effective if the lighting changes. This
Adaptive CBTF (A-CBTF) differs from previous approaches that either do not consider lighting
change over time, or rely on camera transition time information to update. By utilising contextual
information drawn from the background in each camera view, an estimation of the lighting
change within a single camera can be made. This background lighting model allows the mapping
of colour information back to the original training conditions and thus remove the need for
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retraining.
Thirdly, a novel reformulation of re-identification as a ranking problem is proposed. Previous
methods use a score based on a direct distance measure of set features to form a correct/incorrect
match result. Rather than offering an operator a single outcome, the ranking paradigm is to give
the operator a ranked list of possible matches and allow them to make the final decision. By utilising
a Support Vector Machine (SVM) ranking method, a weighting on the appearance features
can be learned that capitalises on the fact that not all image features are equally important to
re-identification. Additionally, an Ensemble-RankSVM is proposed to address scalability issues
by separating the training samples into smaller subsets and boosting the trained models.
Finally, the thesis looks at a practical application of the ranking paradigm in a real world application.
The system encompasses both the re-identification stage and the precursory extraction
and tracking stages to form an aid for CCTV operators. Segmentation and detection are combined
to extract relevant information from the video, while several combinations of matching
techniques are combined with temporal priors to form a more comprehensive overall matching
criteria.
The effectiveness of the proposed approaches is tested on datasets obtained from a variety
of challenging environments including offices, apartment buildings, airports and outdoor public
spaces
Enhanced target detection in CCTV network system using colour constancy
The focus of this research is to study how targets can be more faithfully detected in a multi-camera CCTV network system using spectral feature for the detection. The objective of the work is to develop colour constancy (CC) methodology to help maintain the spectral feature of the scene into a constant stable state irrespective of variable illuminations and camera calibration issues.
Unlike previous work in the field of target detection, two versions of CC algorithms have been developed during the course of this work which are capable to maintain colour constancy for every image pixel in the scene: 1) a method termed as Enhanced Luminance Reflectance CC (ELRCC) which consists of a pixel-wise sigmoid function for an adaptive dynamic range compression, 2) Enhanced Target Detection and Recognition Colour Constancy (ETDCC) algorithm which employs a bidirectional pixel-wise non-linear transfer PWNLTF function, a centre-surround luminance enhancement and a Grey Edge white balancing routine.
The effectiveness of target detections for all developed CC algorithms have been validated using multi-camera βImagery Library for Intelligent Detection Systemsβ (iLIDS), βPerformance Evaluation of Tracking and Surveillanceβ (PETS) and βGround Truth Colour Chartβ (GTCC) datasets. It is shown that the developed CC algorithms have enhanced target detection efficiency by over 175% compared with that without CC enhancement.
The contribution of this research has been one journal paper published in the Optical Engineering together with 3 conference papers in the subject of research
re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification
Conventional approaches to object instance re-identification rely on matching
appearances of the target objects among a set of frames. However, learning
appearances of the objects alone might fail when there are multiple objects
with similar appearance or multiple instances of same object class present in
the scene. This paper proposes that partial observations of the background can
be utilized to aid in the object re-identification task for a rigid scene,
especially a rigid environment with a lot of reoccurring identical models of
objects. Using an extension to the Mask R-CNN architecture, we learn to encode
the important and distinct information in the background jointly with the
foreground relevant to rigid real-world scenarios such as an indoor environment
where objects are static and the camera moves around the scene. We demonstrate
the effectiveness of our joint visual feature in the re-identification of
objects in the ScanNet dataset and show a relative improvement of around 28.25%
in the rank-1 accuracy over the deepSort method.Comment: Accepted to ICIAP 2019 and awarded the Best Student Pape
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.Comment: doctoral dissertatio
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