Détection et localisation d'objets stationnaires par une paire de caméras PTZ

Session "Articles"National audienceDans ce papier, nous proposons une approche originale pour détecter et localiser des objets stationnaires sur une scène étendue en exploitant une paire de caméras PTZ. Nous proposons deux contributions principales. Tout d'abord, nous présentons une méthode de détection et de segmentation d'objets stationnaires. Celle-ci est basée sur la réidentification de descripteurs de l'avant-plan et une segmentation de ces blobs en objets à l'aide de champs de Markov. La seconde contribution concerne la mise en correspondance entre les deux PTZ des silhouettes d'objets détectées dans chaque image

Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware

Visual / acoustic detection and localisation in embedded systems

©Cranfield UniversityThe continuous miniaturisation of sensing and processing technologies is increasingly offering a variety of embedded platforms, enabling the accomplishment of a broad range of tasks using such systems. Motivated by these advances, this thesis investigates embedded detection and localisation solutions using vision and acoustic sensors. Focus is particularly placed on surveillance applications using sensor networks. Existing vision-based detection solutions for embedded systems suffer from the sensitivity to environmental conditions. In the literature, there seems to be no algorithm able to simultaneously tackle all the challenges inherent to real-world videos. Regarding the acoustic modality, many research works have investigated acoustic source localisation solutions in distributed sensor networks. Nevertheless, it is still a challenging task to develop an ecient algorithm that deals with the experimental issues, to approach the performance required by these systems and to perform the data processing in a distributed and robust manner. The movement of scene objects is generally accompanied with sound emissions with features that vary from an environment to another. Therefore, considering the combination of the visual and acoustic modalities would offer a significant opportunity for improving the detection and/or localisation using the described platforms. In the light of the described framework, we investigate in the first part of the thesis the use of a cost-effective visual based method that can deal robustly with the issue of motion detection in static, dynamic and moving background conditions. For motion detection in static and dynamic backgrounds, we present the development and the performance analysis of a spatio- temporal form of the Gaussian mixture model. On the other hand, the problem of motion detection in moving backgrounds is addressed by accounting for registration errors in the captured images. By adopting a robust optimisation technique that takes into account the uncertainty about the visual measurements, we show that high detection accuracy can be achieved. In the second part of this thesis, we investigate solutions to the problem of acoustic source localisation using a trust region based optimisation technique. The proposed method shows an overall higher accuracy and convergence improvement compared to a linear-search based method. More importantly, we show that through characterising the errors in measurements, which is a common problem for such platforms, higher accuracy in the localisation can be attained. The last part of this work studies the different possibilities of combining visual and acoustic information in a distributed sensors network. In this context, we first propose to include the acoustic information in the visual model. The obtained new augmented model provides promising improvements in the detection and localisation processes. The second investigated solution consists in the fusion of the measurements coming from the different sensors. An evaluation of the accuracy of localisation and tracking using a centralised/decentralised architecture is conducted in various scenarios and experimental conditions. Results have shown the capability of this fusion approach to yield higher accuracy in the localisation and tracking of an active acoustic source than by using a single type of data

Advanced traffic video analytics for robust traffic accident detection

Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time. First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road. Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system. The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Computer vision methods applied to person tracking and identification

2013 - 2014Computer vision methods for tracking and identification of people in constrained and unconstrained environments have been widely explored in the last decades. De- spite of the active research on these topics, they are still open problems for which standards and/or common guidelines have not been defined yet. Application fields of computer vision-based tracking systems are almost infinite. Nowadays, the Aug- mented Reality is a very active field of the research that can benefit from vision-based user’s tracking to work. Being defined as the fusion of real with virtual worlds, the success of an augmented reality application is completely dependant on the efficiency of the exploited tracking method. This work of thesis covers the issues related to tracking systems in augmented reality applications proposing a comprehensive and adaptable framework for marker-based tracking and a deep formal analysis. The provided analysis makes possible to objectively assess and quantify the advantages of using augmented reality principles in heterogeneous operative contexts. Two case studies have been considered, that are the support to maintenance in an industrial environment and to electrocardiography in a typical telemedicine scenario. Advan- tages and drawback are provided as well as future directions of the proposed study. The second topic covered in this thesis relates to the vision-based tracking solution for unconstrained outdoor environments. In video surveillance domain, a tracker is asked to handle variations in illumination, cope with appearance changes of the tracked objects and, possibly, predict motion to better anticipate future positions. ... [edited by Author]XIII n.s

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 3 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

Human and Group Activity Recognition from Video Sequences

A good solution to human activity recognition enables the creation of a wide variety of useful applications such as applications in visual surveillance, vision-based Human-Computer-Interaction (HCI) and gesture recognition. In this thesis, a graph based approach to human activity recognition is proposed which models spatio-temporal features as contextual space-time graphs. In this method, spatio-temporal gradient cuboids were extracted at significant regions of activity, and feature graphs (gradient, space-time, local neighbours, immediate neighbours) are constructed using the similarity matrix. The Laplacian representation of the graph is utilised to reduce the computational complexity and to allow the use of traditional statistical classifiers. A second methodology is proposed to detect and localise abnormal activities in crowded scenes. This approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streakline is formed by multiple optical flow vectors that represent and track locally the movement in the scene. A dictionary of activities is recorded for a given scene during the training stage. During the testing stage, the consistency of each observed activity with those from the dictionary is verified using the Kullback-Leibler (KL) divergence. The anomaly detection of the proposed methodology is compared to state of the art, producing state of the art results for localising anomalous activities. Finally, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). The recognition performance of the proposed methodology shows an improvement in recognition performance over state of the art results on group activity datasets

Video object tracking : contributions to object description and performance assessment

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201