This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.

Τα αυτοκινητιστικά δυστυχήματα αποτελούν μια από τις κυριότερες αιτίες θανάτου παγκοσμίως. Ο αυξανόμενος αριθμός τους οδήγησε στην συνειδητοποίηση ότι η χρήση προηγμένης τεχνολογίας για την κατασκευή ασφαλέστερων οχημάτων είναι απαραίτητη για την μείωση των ατυχημάτων και κατά συνέπεια των θανάτων που οφείλονται σε αυτά. Από τη στιγμή που οι τεχνολογικές εξελίξεις επέτρεψαν την ενσωμάτωση φθηνών, χαμηλής κατανάλωσης συστημάτων με μεγάλη επεξεργαστική ταχύτητα σε οχήματα, κατέστη προφανές ότι περίπλοκες τεχνικές υπολογιστικής όρασης μπορούσαν πλέον να χρησιμοποιηθούν για την υποβοήθηση της οδήγησης. Σε αυτή την κατεύθυνση, η παρούσα διατριβή εστιάζει στην ανάπτυξη καινοτόμων λύσεων για διαφορετικά κομμάτια που εμπλέκονται στα προηγμένα συστήματα υποβοήθησης του οδηγού. Πιο συγκεκριμένα, σε αυτή την διατριβή προτείνονται καινοτόμα υποσυστήματα για την αναγνώριση σημάτων οδικής κυκλοφορίας, την αναγνώριση φωτεινών σηματοδοτών, τον εντοπισμό προπορευόμενου οχήματος και τον εντοπισμό δρόμου. Οι τεχνικές που χρησιμοποιήθηκαν για την ανάπτυξη των προτεινόμενων λύσεων βασίζονται στην χρωματική επεξεργασία εικόνας με έμφαση στην ανεξαρτησία από την φωτεινότητα της σκηνής, στην χρήση πληροφορίας συμμετρίας για τον εντοπισμό χαρακτηριστικών αντικειμένων (όπως σήματα οδικής κυκλοφορίας, φωτεινοί σηματοδότες και οχήματα), στην χώρο-χρονική παρακολούθηση των εντοπισμένων αντικειμένων και στην αυτόματη κατάτμηση εικόνας για τον εντοπισμό δρόμου. Τα προτεινόμενα συστήματα αναπτύχθηκαν με στόχο την ανθεκτικότητα σε αλλαγές της φωτεινότητας ή τις καιρικές συνθήκες, καθώς και στην οδήγηση σε απαιτητικά περιβάλλοντα. Επίσης, έχει δοθεί ιδιαίτερη έμφαση στην προοπτική υλοποίησης συστημάτων πραγματικού χρόνου. Τα αποτελέσματα που παρουσιάζονται σε αυτή την διατριβή αποδεικνύουν την ανωτερότητα των προτεινόμενων μεθόδων έναντι αντίστοιχων της σχετικής βιβλιογραφίας, ειδικά στις περιπτώσεις του εντοπισμού προπορευόμενου οχήματος και του εντοπισμού δρόμου. Ελπίζουμε ότι μέρη της έρευνας αυτής θα εμπνεύσουν νέες προσεγγίσεις για τις μελλοντικές υλοποιήσεις αντίστοιχων συστημάτων. / Traffic accidents are one of the main reasons for the loss of human lives worldwide. Their increasing number has led to the realization that the use of advanced technology for manufacturing safer vehicles is imperative for limiting casualties. Since technological breakthroughs allowed the incorporation of cheap, low consumption systems with high processing speeds in vehicles, it became apparent that complex computer vision techniques could be used to assist drivers in navigating their vehicles. In this direction, this thesis focuses on providing novel solutions for different tasks involved in advanced driver assistance systems. More specifically, this thesis proposes novel sub-systems for traffic sign recognition, traffic light recognition, preceding vehicle detection and road detection. The techniques used for developing the proposed solutions are based on color image processing with a focus on illumination invariance, using symmetry information for man-made objects (like traffic signs, traffic lights and vehicles) detection, spatiotemporal tracking of detected results and automated image segmentation for road detection. The proposed systems were implemented with a goal of robustness to changes of illumination and weather conditions, as well as to diverse driving environments. A special focus on the prospect for real-time implementation has also been given. The results presented in this thesis indicate the superiority of the proposed methods to their counterparts found in relevant literature in both normal and challenging conditions, especially in the cases of preceding vehicle detection and road detection. Hopefully, parts of this research will provide new insights for future developments in the field of intelligent transportation.

This thesis aims at proposing vehicle license plate detection and recognition algorithms, suitable for vehicle re-identification. Simple urban traffic analysis system is also proposed. Multiple stages of this system was developed and tested. Specifically - vehicle detection, license plate detection and recognition. Vehicle detection is based on background substraction method, which results in an average hit rate of ~92%. License plate detection is done by cascade classifiers and achieves an average hit rate of 81.92% and precision rate of 94.42%. License plate recognition based on Template matching results in an average precission rate of 60.55%. Therefore the new license plate recognition method based on license plate scanning using the sliding window principle and neural network recognition was introduced. Neural network achieves a precision rate of 64.47% for five input features. Low precision rate of neural network is caused by small amount of training sample for some specific license plate characters.

Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance.

Research and Development engineering is at the corner stone of humanity’s evolution. It is perceived to be a systematic creative process which ultimately improves the living standard of a society through the creation of new applications and products. The commercial paradigm that governs project selection, resource allocation and market penetration prevails when the focus shifts from pure research to applied research. Furthermore, the road to success through commercialisation is difficult for most inventors, especially in a vast and isolated country such as Australia which is located a long way from wealthy and developed economies. While market leading products are considered unique, the actual process to achieve these products is essentially the same; progressing from an idea, through development to an outcome (if successful). Unfortunately, statistics indicate that only 3% of ‘ideas’ are significantly successful, 4% are moderately successful, and the remainder ‘evaporate’ in that form (Michael Quinn, Chairman, Innovation Capital Associates Pty Ltd). This study demonstrates and analyses two techniques developed by the author which reduce uncertainty in the engineering design and development phase of new product development and therefore increase the probability of a successful outcome. This study expands the existing knowledge of the engineering design and development stage in the new product development process and is couched in the identification of practical methods, which have been successfully used to develop new products by Australian Small Medium Enterprise (SME) Excel Technology Group Pty Ltd (ETG). Process theory is the term most commonly used to describe scientific study that identifies occurrences that result from a specified input state to an output state, thus detailing the process used to achieve an outcome. The thesis identifies relevant material and analyses recognised and established engineering processes utilised in developing new products. The literature identified that case studies are a particularly useful method for supporting problem-solving processes in settings where there are no clear answers or where problems are unstructured, as in New Product Development (NPD). This study describes, defines, and demonstrates the process of new product development within the context of historical product development and a ‘live’ case study associated with an Australian Government START grant awarded to Excel Technology Group in 2004 to assist in the development of an image-based vehicle detection product. This study proposes two techniques which reduce uncertainty and thereby improve the probability of a successful outcome. The first technique provides a predicted project development path or forward engineering plan which transforms the initial ‘fuzzy idea’ into a potential and achievable outcome. This process qualifies the ‘fuzzy idea’ as a potential, rationale or tangible outcome which is within the capability of the organisation. Additionally, this process proposes that a tangible or rationale idea can be deconstructed in reverse engineering process in order to create a forward engineering development plan. A detailed structured forward engineering plan reduces the uncertainty associated with new product development unknowns and therefore contributes to a successful outcome. This is described as the RETRO technique. The study recognises however that this claim requires qualification and proposes a second technique. The second technique proposes that a two dimensional spatial representation which has productivity and consumed resources as its axes, provides an effective means to qualify progress and expediently identify variation from the predicted plan. This spatial representation technique allows a quick response which in itself has a prediction attribute associated with directing the project back onto its predicted path. This process involves a coterminous comparison between the predicted development path and the evolving actual project development path. A consequence of this process is verification of progress or the application of informed, timely and quantified corrective action. This process also identifies the degree of success achieved in the engineering design and development phase of new product development where success is defined as achieving a predicted outcome. This spatial representation technique is referred to as NPD Mapping. The study demonstrates that these are useful techniques which aid SMEs in achieving successful new product outcomes because the technique are easily administered, measure and represent relevant development process related elements and functions, and enable expedient quantified responsive action when the evolving path varies from the predicted path. These techniques go beyond time line representations as represented in GANTT charts and PERT analysis, and represent the base variables of consumed resource and productivity/technical achievement in a manner that facilitates higher level interpretation of time, effort, degree of difficulty, and product complexity in order to facilitate informed decision making. This study presents, describes, analyses and demonstrates an SME focused engineering development technique, developed by the author, that produces a successful new product outcome which begins with a ‘fuzzy idea’ in the mind of the inventor and concludes with a successful new product outcome that is delivered on time and within budget. Further research on a wider range of SME organisations undertaking new product development is recommended.