Key-point based tracking for illegally parked vehicle detection

This research aims to develop a target detection and tracking system that can realize real-time video surveillance. The purpose of the research is to realize a monitoring application that can run automatically and intelligently to detect and track illegally parked vehicles. Since the application scenario of the algorithm is a real traffic environment, it must be able to adapt to complex environmental interference, such as drastic changes in lighting conditions, frequent occlusion, and long-term stable tracking. The thesis shows the detailed design process and test results of the system. This algorithm combines the target detection function based on deep learning network and the multi-object tracking algorithm based on key point matching. The method shown in the thesis focuses on detecting and tracking stationary vehicles in the no parking area. An object detection algorithm based on a deep learning network is used to recognize vehicles. Once the recognized vehicle is defined as an illegally parked vehicle through the determination of its motion state and location, an algorithm based on key-point matching is developed and tracked for this type of vehicle. If the target is still stationary in the no parking area after a period, the system will generate an alarm. The method was tested in more than 20 hours of video. The video comes from public database and our own. They all show real surveillance scenes, including different time periods of the day and different locations. The test results show that the method achieves 100% in precision (also called positive predictive value), 95% in recall (also known as sensitivity) and 97% in F1 (a measure that combines precision and recall). The results obtained also produce better detection and tracking compared to other comparable methods

Detection of Parked Vehicles using Spatio-temporal Maps

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels. © 2011 IEEE.This work was supported by the Spanish Government project Movilidad y automocion en Redes de Transporte Avanzadas (MARTA) under the Consorcios Estrategicos Nacionales de Investigacion Tecnologica (CENIT) program and the Comision Interministerial Ciencia Y Tecnologia (CICYT) under Contract TEC2009-09146. The Associate Editor for this paper was R. W. Goudy.Albiol Colomer, AJ.; Sanchis Pastor, L.; Albiol Colomer, A.; Mossi García, JM. (2011). Detection of Parked Vehicles using Spatio-temporal Maps. IEEE Transactions on Intelligent Transportation Systems. 12(4):1277-1291. https://doi.org/10.1109/TITS.2011.2156791S1277129112

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and Automation (ICRA), 201

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.Comment: To appear on ICRA 201