Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles

We present a novel, realtime algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number of particles allocated to each pedestrian based on different confidence metrics. Additionally, we use a new high-definition crowd video dataset, which is used to evaluate the performance of different pedestrian tracking algorithms. This dataset consists of videos of indoor and outdoor scenes, recorded at different locations with 30-80 pedestrians. We highlight the performance benefits of our algorithm over prior techniques using this dataset. In practice, our algorithm can compute trajectories of tens of pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per second). To the best of our knowledge, our approach is 4-5 times faster than prior methods, which provide similar accuracy

Pedestrian Detection and Tracking in Urban Context Using a Mono-camera

Jalakäijate tuvastus ja jälgimine on üks tähtsamaid aspekte edasijõudnud sõitja abisüsteemides. Need süsteemid aitavad vältida ohtlikke olukordi, juhendades sõitjaid ja hoiatades ettetulevate riskide eest. Jalakäijate tuvastuse ja jälgimise põhiideed on tuvastada jalakäijad siis, kui nad on turvalises tsoonis ja ennustada nende asukohta ja suunda. Selle lõputöö eesmärk on uurida võimalikke meetodeid ja arendada nende põhjal hea algoritm jalakäijate tuvastuseks ja jälgimiseks.Selles lõputöös arendatud lahendus keskendub jalakäija täpsele tuvastamisele ja jälgimisele. Süsteemi täpsuse hindamiseks on saadud tulemusi võrreldud olemasolevate lahendustega.Pedestrian detection and tracking are one of the important aspects in Advanced Driver Assistance Systems. These systems help to avoid dangerous situations, by guiding drivers and warning them about the upcoming risks. The main ideas of pedestrian detection and tracking are to detect pedestrians, while they are in the secure zone, and predict their position and direction.The goal of this thesis is to examine possible methods and based on these, to develop a good pedestrian detection and tracking algorithm. The solution developed in this thesis, focuses on accurately detecting and tracking a pedestrian. In order to estimate the accuracy of the system, obtained results will be compared to the existing solutions

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Coffee Queue Project

In this paper, a computer vision system for counting people standing in line is presented. In this application, common techniques such as Adaptive Background Subtraction (ABS), blob tracking with Kalman filter, and occlusion resistive techniques are used to detect and track people. Additionally, a novel method using Dual Adaptive Background Subtractors (DABS) is implemented for dynamically determining the line region in a real-world crowded scene, and also as an alternative target acquisition to regular ABS. The DABS technique acts as a temporal bandpass filter for motion, helping identify people standing in line while in the presence of other moving people. This is achieved by using two ABS with different temporal adaptiveness. Unlike other computer vision papers which perform tests in highly controlled environments, the DABS technique is tested in a crowded Starbucks© at the Cal Poly student union. For any length of people standing in line, result shows that DABS has a lower mean error by one or more people when compared to ABS. Even in challenging crowded scenes where the line can reach 19 people in length, DABS achieves a Normalized RMS Error of 43%

Radar-based Application of Pedestrian and Cyclist Micro-Doppler Signatures for Automotive Safety Systems

Die sensorbasierte Erfassung des Nahfeldes im Kontext des hochautomatisierten Fahrens erfährt einen spürbaren Trend bei der Integration von Radarsensorik. Fortschritte in der Mikroelektronik erlauben den Einsatz von hochauflösenden Radarsensoren, die durch effiziente Verfahren sowohl im Winkel als auch in der Entfernung und im Doppler die Messgenauigkeit kontinuierlich ansteigen lassen. Dadurch ergeben sich neuartige Möglichkeiten bei der Bestimmung der geometrischen und kinematischen Beschaffenheit ausgedehnter Ziele im Fahrzeugumfeld, die zur gezielten Entwicklung von automotiven Sicherheitssystemen herangezogen werden können. Im Rahmen dieser Arbeit werden ungeschützte Verkehrsteilnehmer wie Fußgänger und Radfahrer mittels eines hochauflösenden Automotive-Radars analysiert. Dabei steht die Erscheinung des Mikro-Doppler-Effekts, hervorgerufen durch das hohe Maß an kinematischen Freiheitsgraden der Objekte, im Vordergrund der Betrachtung. Die durch den Mikro-Doppler-Effekt entstehenden charakteristischen Radar-Signaturen erlauben eine detailliertere Perzeption der Objekte und können in direkten Zusammenhang zu ihren aktuellen Bewegungszuständen gesetzt werden. Es werden neuartige Methoden vorgestellt, die die geometrischen und kinematischen Ausdehnungen der Objekte berücksichtigen und echtzeitfähige Ansätze zur Klassifikation und Verhaltensindikation realisieren. Wird ein ausgedehntes Ziel (z.B. Radfahrer) von einem Radarsensor detektiert, können aus dessen Mikro-Doppler-Signatur wesentliche Eigenschaften bezüglich seines Bewegungszustandes innerhalb eines Messzyklus erfasst werden. Die Geschwindigkeitsverteilungen der sich drehenden Räder erlauben eine adaptive Eingrenzung der Tretbewegung, deren Verhalten essentielle Merkmale im Hinblick auf eine vorausschauende Unfallprädiktion aufweist. Ferner unterliegen ausgedehnte Radarziele einer Orientierungsabhängigkeit, die deren geometrischen und kinematischen Profile direkt beeinflusst. Dies kann sich sowohl negativ auf die Klassifikations-Performance als auch auf die Verwertbarkeit von Parametern auswirken, die eine Absichtsbekundung des Radarziels konstituieren. Am Beispiel des Radfahrers wird hierzu ein Verfahren vorgestellt, das die orientierungsabhängigen Parameter in Entfernung und Doppler normalisiert und die gemessenen Mehrdeutigkeiten kompensiert. Ferner wird in dieser Arbeit eine Methodik vorgestellt, die auf Grundlage des Mikro- Doppler-Profils eines Fußgängers dessen Beinbewegungen über die Zeit schätzt (Tracking) und wertvolle Objektinformationen hinsichtlich seines Bewegungsverhaltens offenbart. Dazu wird ein Bewegungsmodell entwickelt, das die nichtlineare Fortbewegung des Beins approximiert und dessen hohes Maß an biomechanischer Variabilität abbildet. Durch die Einbeziehung einer wahrscheinlichkeitsbasierten Datenassoziation werden die Radar-Detektionen ihren jeweils hervorrufenden Quellen (linkes und rechtes Bein) zugeordnet und eine Trennung der Gliedmaßen realisiert. Im Gegensatz zu bisherigen Tracking-Verfahren weist die vorgestellte Methodik eine Steigerung in der Genauigkeit der Objektinformationen auf und stellt damit einen entscheidenden Vorteil für zukünftige Fahrerassistenzsysteme dar, um deutlich schneller auf kritische Verkehrssituationen reagieren zu können.:1 Introduction 1 1.1 Automotive environmental perception 2 1.2 Contributions of this work 4 1.3 Thesis overview 6 2 Automotive radar 9 2.1 Physical fundamentals 9 2.1.1 Radar cross section 9 2.1.2 Radar equation 10 2.1.3 Micro-Doppler effect 11 2.2 Radar measurement model 15 2.2.1 FMCW radar 15 2.2.2 Chirp sequence modulation 17 2.2.3 Direction-of-arrival estimation 22 2.3 Signal processing 25 2.3.1 Target properties 26 2.3.2 Target extraction 28 Power detection 28 Clustering 30 2.3.3 Real radar data example 31 2.4 Conclusion 33 3 Micro-Doppler applications of a cyclist 35 3.1 Physical fundamentals 35 3.1.1 Micro-Doppler signatures of a cyclist 35 3.1.2 Orientation dependence 36 3.2 Cyclist feature extraction 38 3.2.1 Adaptive pedaling extraction 38 Ellipticity constraints 38 Ellipse fitting algorithm 39 3.2.2 Experimental results 42 3.3 Normalization of the orientation dependence 44 3.3.1 Geometric correction 44 3.3.2 Kinematic correction 45 3.3.3 Experimental results 45 3.4 Conclusion 47 3.5 Discussion and outlook 47 4 Micro-Doppler applications of a pedestrian 49 4.1 Pedestrian detection 49 4.1.1 Human kinematics 49 4.1.2 Micro-Doppler signatures of a pedestrian 51 4.1.3 Experimental results 52 Radially moving pedestrian 52 Crossing pedestrian 54 4.2 Pedestrian feature extraction 57 4.2.1 Frequency-based limb separation 58 4.2.2 Extraction of body parts 60 4.2.3 Experimental results 62 4.3 Pedestrian tracking 64 4.3.1 Probabilistic state estimation 65 4.3.2 Gaussian filters 67 4.3.3 The Kalman filter 67 4.3.4 The extended Kalman filter 69 4.3.5 Multiple-object tracking 71 4.3.6 Data association 74 4.3.7 Joint probabilistic data association 80 4.4 Kinematic-based pedestrian tracking 84 4.4.1 Kinematic modeling 84 4.4.2 Tracking motion model 87 4.4.3 4-D radar point cloud 91 4.4.4 Tracking implementation 92 4.4.5 Experimental results 96 Longitudinal trajectory 96 Crossing trajectory with sudden turn 98 4.5 Conclusion 102 4.6 Discussion and outlook 103 5 Summary and outlook 105 5.1 Developed algorithms 105 5.1.1 Adaptive pedaling extraction 105 5.1.2 Normalization of the orientation dependence 105 5.1.3 Model-based pedestrian tracking 106 5.2 Outlook 106 Bibliography 109 List of Acronyms 119 List of Figures 124 List of Tables 125 Appendix 127 A Derivation of the rotation matrix 2.26 127 B Derivation of the mixed radar signal 2.52 129 C Calculation of the marginal association probabilities 4.51 131 Curriculum Vitae 135Sensor-based detection of the near field in the context of highly automated driving is experiencing a noticeable trend in the integration of radar sensor technology. Advances in microelectronics allow the use of high-resolution radar sensors that continuously increase measurement accuracy through efficient processes in angle as well as distance and Doppler. This opens up novel possibilities in determining the geometric and kinematic nature of extended targets in the vehicle environment, which can be used for the specific development of automotive safety systems. In this work, vulnerable road users such as pedestrians and cyclists are analyzed using a high-resolution automotive radar. The focus is on the appearance of the micro-Doppler effect, caused by the objects’ high kinematic degree of freedom. The characteristic radar signatures produced by the micro-Doppler effect allow a clearer perception of the objects and can be directly related to their current state of motion. Novel methods are presented that consider the geometric and kinematic extents of the objects and realize real-time approaches to classification and behavioral indication. When a radar sensor detects an extended target (e.g., bicyclist), its motion state’s fundamental properties can be captured from its micro-Doppler signature within a measurement cycle. The spinning wheels’ velocity distributions allow an adaptive containment of the pedaling motion, whose behavior exhibits essential characteristics concerning predictive accident prediction. Furthermore, extended radar targets are subject to orientation dependence, directly affecting their geometric and kinematic profiles. This can negatively affect both the classification performance and the usability of parameters constituting the radar target’s intention statement. For this purpose, using the cyclist as an example, a method is presented that normalizes the orientation-dependent parameters in range and Doppler and compensates for the measured ambiguities. Furthermore, this paper presents a methodology that estimates a pedestrian’s leg motion over time (tracking) based on the pedestrian’s micro-Doppler profile and reveals valuable object information regarding his motion behavior. To this end, a motion model is developed that approximates the leg’s nonlinear locomotion and represents its high degree of biomechanical variability. By incorporating likelihood-based data association, radar detections are assigned to their respective evoking sources (left and right leg), and limb separation is realized. In contrast to previous tracking methods, the presented methodology shows an increase in the object information’s accuracy. It thus represents a decisive advantage for future driver assistance systems in order to be able to react significantly faster to critical traffic situations.:1 Introduction 1 1.1 Automotive environmental perception 2 1.2 Contributions of this work 4 1.3 Thesis overview 6 2 Automotive radar 9 2.1 Physical fundamentals 9 2.1.1 Radar cross section 9 2.1.2 Radar equation 10 2.1.3 Micro-Doppler effect 11 2.2 Radar measurement model 15 2.2.1 FMCW radar 15 2.2.2 Chirp sequence modulation 17 2.2.3 Direction-of-arrival estimation 22 2.3 Signal processing 25 2.3.1 Target properties 26 2.3.2 Target extraction 28 Power detection 28 Clustering 30 2.3.3 Real radar data example 31 2.4 Conclusion 33 3 Micro-Doppler applications of a cyclist 35 3.1 Physical fundamentals 35 3.1.1 Micro-Doppler signatures of a cyclist 35 3.1.2 Orientation dependence 36 3.2 Cyclist feature extraction 38 3.2.1 Adaptive pedaling extraction 38 Ellipticity constraints 38 Ellipse fitting algorithm 39 3.2.2 Experimental results 42 3.3 Normalization of the orientation dependence 44 3.3.1 Geometric correction 44 3.3.2 Kinematic correction 45 3.3.3 Experimental results 45 3.4 Conclusion 47 3.5 Discussion and outlook 47 4 Micro-Doppler applications of a pedestrian 49 4.1 Pedestrian detection 49 4.1.1 Human kinematics 49 4.1.2 Micro-Doppler signatures of a pedestrian 51 4.1.3 Experimental results 52 Radially moving pedestrian 52 Crossing pedestrian 54 4.2 Pedestrian feature extraction 57 4.2.1 Frequency-based limb separation 58 4.2.2 Extraction of body parts 60 4.2.3 Experimental results 62 4.3 Pedestrian tracking 64 4.3.1 Probabilistic state estimation 65 4.3.2 Gaussian filters 67 4.3.3 The Kalman filter 67 4.3.4 The extended Kalman filter 69 4.3.5 Multiple-object tracking 71 4.3.6 Data association 74 4.3.7 Joint probabilistic data association 80 4.4 Kinematic-based pedestrian tracking 84 4.4.1 Kinematic modeling 84 4.4.2 Tracking motion model 87 4.4.3 4-D radar point cloud 91 4.4.4 Tracking implementation 92 4.4.5 Experimental results 96 Longitudinal trajectory 96 Crossing trajectory with sudden turn 98 4.5 Conclusion 102 4.6 Discussion and outlook 103 5 Summary and outlook 105 5.1 Developed algorithms 105 5.1.1 Adaptive pedaling extraction 105 5.1.2 Normalization of the orientation dependence 105 5.1.3 Model-based pedestrian tracking 106 5.2 Outlook 106 Bibliography 109 List of Acronyms 119 List of Figures 124 List of Tables 125 Appendix 127 A Derivation of the rotation matrix 2.26 127 B Derivation of the mixed radar signal 2.52 129 C Calculation of the marginal association probabilities 4.51 131 Curriculum Vitae 13

Detecting and tracking people in real-time

The problem of detecting and tracking people in images and video has been the subject of a great deal of research, but remains a challenging task. Being able to detect and track people would have an impact in a number of fields, such as driverless vehicles, automated surveillance, and human-computer interaction. The difficulties that must be overcome include coping with variations in appearance between different people, changes in lighting, and the ability to detect people across multiple scales. As well as having high accuracy, it is desirable for a technique to evaluate an image with low latency between receiving the image and producing a result. This thesis explores methods for detecting and tracking people in images and video. Techniques are implemented on a desktop computer, with an emphasis on low latency. The problem of detection is examined first. The well established integral channel features detector is introduced and reimplemented, and various novelties are implemented in regards to the features used by the detector. Results are given to quantify the accuracy and the speed of the developed detectors on the INRIA person dataset. The method is further extended by examining the prospect of using multiple classifiers in conjunction. It is shown that using a classifier with a version of the same classifier reflected in the vertical axis can improve performance. A novel method for clustering images of people to find modes of appearance is also presented. This involves using boosting classifiers to map a set of images to vectors, to which K-means clustering is applied. Boosting classifiers are then trained on these clustered datasets to create sets of multiple classifiers, and it is demonstrated that these sets of classifiers can be evaluated on images with only a small increase in the running time over single classifiers. The problem of single target tracking is addressed using the mean shift algorithm. Mean shift tracking works by finding the best colour match for a target from frame to frame. A novel form of mean shift tracking through scale is developed, and the problem of multiple target tracking is addressed by using boosting classifiers in conjunction with Kalman filters. Tests are carried out on the CAVIAR dataset, which gives representative examples of surveillance scenarios, to show the performance of the proposed approaches.Open Acces