The car has become bigger, faster and more advanced for each passing year since its first appearance, and the safety requirements have also become stricter. Computer vision based support is a growing area of safety features where the car is equipped with a mono- or stereo camera. It can be used for detecting pedestrians walking out in the street, give a warning for wild-life during a cold January night using night-vision cameras and much more. This master thesis investigates the problem of detecting and tracking overtaking vehicles. Vehicles that overtake are only partly visible in the beginning, rendering it hard for standard detection/classification algorithms to get a positive detection. The need to quickly detect an incoming vehicle is crucial to be able to take fast counter-measure, such as braking, if needed. A novel approach referred to as the \textit{Wall detector} is suggested, detecting incoming vehicles using one-dimensional optical flow. Under the assumption that an overtaking car is moving in parallel to the ego-vehicle, both cars are moving towards the vanishing point in the image. A detection wall, consisting of several detection lines moving towards the vanishing point, is created, making all objects that are moving parallel to the ego-vehicle move along these lines. The result is a light-weight and fast detector with good detection performance in real-time. Several approaches for the Wall detector are implemented and evaluated, revealing that a feature based approach is the best choice. The information from the system can be used as input to heavier algorithms, boosting the confidence or to initialize a track.

In this thesis, we present a vehicle detection and tracking system based on image processing and pattern recognition of single camera image sequences. Both software design and hardware implementation are considered. In the hypothesis generation (HG) step and the hypothesis verification (HV) step, we use the shadow detection technique combined with the proposed constrained vehicle width/distance ratio to eliminate unreasonable hypotheses. Furthermore, we use SVM classifier, a popular machine learning technique, to verify the generated hypothesis more precisely. In the vehicle tracking step, we limit vehicle tracking duration and periodic vehicle detection mechanisms. These tracking methods alleviate our driver-assistant system from executing complex operations of vehicle detection repeatedly and thus increase system performance without sacrificing too much in case of tracking wrong objects. Based on the the profiling of the software execution time, we implement by hardware the most critical part, the preprocessing of intensity conversion and edge detection. The complete software/hardware embedded system is realized in a FPGA prototype board, so that performance of whole system could achieve real-time processing without too much hardware cost.

Η ανάπτυξη ενσωματωμένων στο όχημα συστημάτων υποβοήθησης του οδηγού για αποφυγή συγκρούσεων με άλλα οχήματα έχει βρεθεί στο επίκεντρο του ενδιαφέροντος εξαιτίας των μεγάλων απωλειών σε ανθρώπινες ζωές στα αυτοκινητιστικά δυστυχήματα. Τα βασισμένα στην όραση συστήματα εξελίσσονται με πολύ γρήγορους ρυθμούς αλλά η έλλειψη μεγάλης σθεναρότητας αποτελεί τροχοπέδη στην τοποθέτηση τους σε οχήματα ευρείας παραγωγής. Ωστόσο, τα αποτελέσματα των ερευνών είναι πολύ ενθαρρυντικά. Στην παρούσα διπλωματική εργασία στόχος είναι η ανάπτυξη ενός συστήματος αναγνώρισης οχημάτων από εικόνες που λαμβάνονται από βιντεοκάμερα που έχει ενσωματωθεί στο όχημα. Η προσπάθεια ανίχνευσης πραγματοποιήθηκε έχοντας ως μοναδικό στοιχείο της εικόνες αυτές , τόσο τα κατασκευαστικά χαρακτηριστικά της κάμερας όσο και η ταχύτητα του οχήματος δεν ήταν γνωστά. Η μεθοδολογία που επιλέχθηκε είναι αυτή της οπτικής ροής. Αρχικά υπολογίσθηκε η οπτική ροή μεταξύ δύο καρέ και στην συνέχεια αυτή η πληροφορία αξιοποιήθηκε με δύο μεθόδους ώστε να γίνει η υπόθεση για τη θέση του κινούμενου οχήματος στην εικόνα. Η μία χρησιμοποιεί την κλίση της οπτικής ροής σε συνδυασμό με την απότομη αλλαγή της κατεύθυνσης των διανυσμάτων οπτικής ροής στα άκρα των κινούμενων οχημάτων και η άλλη συνδυάζει την οπτική ροή με την ανάλυση κύριων τμημάτων (PCA-Principal Component Analysis). / The goal of this paper is the development of an on-board vehicle detection system with the use of optical flow techniques.

The primary objective of this work is to design a parking guidance system to reliably detect entering/exiting vehicles to a parking garage in a cost-efficient manner. Existing solutions (inductive loops, RFID based systems, and video image processors) at shopping malls, universities, airports etc., are expensive due to high installation and maintenance costs. There is a need for a parking guidance system that is reliable, accurate, and cost-effective. The proposed parking guidance system is designed to optimize the use of parking spaces and to reduce wait times. Based on a literature review we identify that the ultrasonic sensor is suitable to detect an entering/exiting vehicle. Initial experiments were performed to test the sensor using an Arduino based embedded system. Detection logic was then developed to identify a car after analyzing the initial test results. This logic was extended to trigger a camera to take an image of the vehicle for validation purposes. This system consists of Arduino, ultrasonic sensor, and a temperature sensor. It was installed and tested in Richard Beard Garage at the University of South Florida for five days. The test results of each trial are provided and average error for all the trials is calculated. The error cases occur due to golf carts, straddling cars on both entry/exit lanes, and people walking under the sensor. The average error of the system is 5.36% over five days (120 hrs). The estimated cost for one detector per lane is approximately $30.

This diploma thesis deals with the detection of moving objects in a video recording using neural networks. The aim of the thesis was to detect road users in video recordings. Pre-trained YOLOv5 object detection model was used for a practical part of the thesis. As part of the solution, an own dataset of traffic road video recordings was created and annotated with following classes: a car, a bus, a van, a motorcycle, a truck and a trailer truck. Final version of this dataset comprise 5404 frames and 6467 annotated objects in total. After training, the YOLOv5 model achieved 0.995 mAP, 0.995 precision and 0.986 recall on the dataset. All steps leading to the final form of the dataset are described in the conclusion chapter.

The article introduces a general survey of concepts used in traffic monitoring applications. It describes different approaches for solving particular steps of vehicle detection process. Analysis of these methods was performed. Furthermore this work focuses on the design and realization of complex robust algorithm for real-time vehicle detection. It is based on analysis of video-sequence acquired from static camera situated on highway. Processing consists of many steps. It starts with background subtraction and ends with traffic monitoring results, i.e. average speed, number of cars, level of service etc.

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Computer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the-art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle detection are developing techniques to solve this problem. This thesis proposes an advanced vehicle detection model, adopting one of the classical neural networks, which are the residual neural network and the region proposal network. The model utilizes the residual neural network as a feature extractor and the region proposal network to detect the potential objects' information. / Master of Science / Computer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle detection are developing techniques to solve this problem. This thesis proposes an advanced vehicle detection model, utilizing deep learning techniques to detect the potential objects’ information.

This report presents a method for estimating the presence and duration of preceding and lead vehicle in front of a motorcycle using an object detection algorithm guided by radar data. The video and radar data were collected as part of a large transportation project. The data are recorded by the ego vehicle during a trip while in a naturalistic research study. The goal is to validate objects detected by radar using vision, to identify moving preceding vehicles and the lead vehicle. The proposed approach takes advantage of radar data in locating the vehicles and other targets and then validates the targets as vehicles using Dual-Tree Branch-and-Bound (Kokkinos, 2011) object detection algorithm. Localization, detection and tracking took 0.0385 seconds per frame on average. Precision and recall of lead vehicle detection is 98.61% and 90.53% respectively. The algorithm presents a comprehensive approach to localize target vehicles in video. The radar object coordinates are mapped on the video frame using perspective projection map- ping. Then persistent radar objects are determined by analyzing their trajectory on video frames. When a radar object appears for three consecutive frames, its called a persistent object. A region of interest (ROI) around the persistent radar object is cropped from the frame, and passed to the object detection algorithm to determine if the persistent object is a car. Once a car is detected the validation of the radar object is complete. We track the detected car in the following frames and refresh the detection after every fourteen frames. The car detection algorithm runs whenever a new persistent radar object is introduced. After validating radar objects, at each timestamp, the lead vehicle is determined using radar object's forward and lateral distance. The time from detecting a lead vehicle to the time when the vehicle disappears or another vehicle becomes lead vehicle, is recorded to get the epochs of following driving mode for that lead vehicle. Finally, the detection result is integrated with MATLAB lane detection system to make a complete system for lead vehicle detection and tracking. The video of interest has 240x720 resolution and approximately 15 frames per second. The car detection algorithm takes 0.1960 seconds on average to detect one car in a machine with Windows operating system and 4GB RAM. But as the detection algorithm is not run for each frame it saves time. Since no annotated motorcycle video dataset is publicly available, two videos of 52 seconds and 26 seconds were manually annotated to test the performance of the approach. The current approach works almost at real time. The algorithm has been tested and results have been reported on 1 video. / Master of Science