Origin-Destination (O-D) Trip Table Estimation Using Traffic Movement Counts from Vehicle Tracking System at Intersection

A new video-based vehicle tracking system is proposed to provide accurate information on directional traffic counts at intersections. The extracted counts are fed to estimate an origin- destination trip table which is necessary information for traffic impact study and transportation planning. The system utilizes a fisheye lens to expand the area covered by a single camera and uses a particle filtering method to track an individual vehicle. The filter is designed to handle environmental changes and multiple motion dynamics. Experimental results show its strong ability on tracking in various conditions. The paper shows how to use the tracking outputs in obtaining accurate origin-destination (O-D) table

Visual Traffic Movement Counts at Intersection for Origin-Destination (O-D) Trip Table Estimation

Origin-destination (O-D) trip table is necessary information for transportation planning and traffic impact study. However, current O-D estimations rely on estimated directional traffic counts at intersections, which obviously diminish reliability in the table. A video-based vehicle tracking system that utilizes wide view-angle lenses has been studied [26] to provide accurate direction traffic counts. The system expands the area covered by a single camera and uses a particle filtering method to handle environmental changes as well as geometric distortion caused by those lenses. This paper shows tracking capability of the system and also shows how to incorporate the directional traffic counts in the O-D estimation

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080$\times$1080 and 1280$\times$1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640$\times$640 and 1280$\times$1280, respectively. The dataset will be available on GitHub with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.Comment: CVPR Workshops 202

Detecting and Tracking Vulnerable Road Users\u27 Trajectories Using Different Types of Sensors Fusion

Vulnerable road user (VRU) detection and tracking has been a key challenge in transportation research. Different types of sensors such as the camera, LiDAR, and inertial measurement units (IMUs) have been used for this purpose. For detection and tracking with the camera, it is necessary to perform calibration to obtain correct GPS trajectories. This method is often tedious and necessitates accurate ground truth data. Moreover, if the camera performs any pan-tilt-zoom function, it is usually necessary to recalibrate the camera. In this thesis, we propose camera calibration using an auxiliary sensor: ultra-wideband (UWB). USBs are capable of tracking a road user with ten-centimeter-level accuracy. Once a VRU with a UWB traverses in the camera view, the UWB GPS data is fused with the camera to perform real-time calibration. As the experimental results in this thesis have shown, the camera is able to output better trajectories after calibration. It is expected that the use of UWB is needed only once to fuse the data and determine the correct trajectories at the same intersection and location of the camera. All other trajectories collected by the camera can be corrected using the same adjustment. In addition, data analysis was conducted to evaluate the performance of the UWB sensors. This study also predicted pedestrian trajectories using data fused by the UWB and smartphone sensors. UWB GPS coordinates are very accurate although it lacks other sensor parameters such as accelerometer, gyroscope, etc. The smartphone data have been used in this scenario to augment the UWB data. The two datasets were merged on the basis of the closest timestamp. The resulting dataset has precise latitude and longitude from UWB as well as the accelerometer, gyroscope, and speed data from smartphones making the fused dataset accurate and rich in terms of parameters. The fused dataset was then used to predict the GPS coordinates of pedestrians and scooters using LSTM

Automated Automotive Radar Calibration With Intelligent Vehicles

While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.Comment: 5 pages, 4 figures, accepted for presentation at the 31st European Signal Processing Conference (EUSIPCO), September 4 - September 8, 2023, Helsinki, Finlan

Algorithms for LiDAR Based Traffic Tracking: Development and Demonstration

The current state of the art of traffic tracking is based on the use of video, and requires extensive manual intervention for it to work, including hours of painstaking human examination of videos frame by frame which also make the acquisition of data extremely expensive. Fundamentally, this is because we do not have observability of the actual scene from a camera which captures a 2D projection of the 3D world. Even if video were to be automated, it would involve such algorithms as RANSACK for outlier elimination while matching features across frames or across multiple cameras. This results in algorithms without stationary relationships between input and output statistics, i.e., between sensing resolution and error and estimated positions and velocities. LiDAR directly provides 3D point clouds, giving a one-one mapping between the scene from the physical world and data. However, available eye-safe lidars have been developed for autonomous vehicles, and provide only sparse point clouds when used for longer range data acquisition. Our experimental results use the Velodyne HDL 64E lidar. The sparse nature of data points returned by the Velodyne LiDAR rendered most of the algorithms for object identification and tracking using 3D point clouds at the point cloud library (PCL), a leading multi-agency open source research initiative focused on 3D point cloud processing ineffective for our work. Hence I developed a comprehensive set of algorithms developed to identify and remove background; detect objects through clustering of remaining points; associate detected objects across frames, track the detected objects, and estimate the dimension of objects. Two different complementary algorithms based on, surface equation (in 3D Cartesian coordinates) and LiDAR spherical coordinates were developed for background identification and removal. Delaunay triangulation based clustering is performed to identify objects. Kalman filter and Hungarian assignment algorithm are used in tandem to track multiple objects simultaneously. A novel bounding box algorithm was devised taking advantage of the way LiDAR scans the environment to predict the orientation and estimate dimension of objects. Trajectory analysis is performed to identify and split any wrong associations, join trajectories belonging to same object and stitch partial trajectories. Finally, the results are stored in a format usable by various transportation or traffic engineering applications. The algorithms were tested by peers with data collected at three intersections. Detection rate and counting accuracy are above 95% which is on par with commercial video solutions that employ humans to varying degrees. While prototyping for the algorithms was done it MATLAB, preliminary tests of conversion to C++ showed that the developed algorithms can be executed in real time on standard computer hardware

TScan: Stationary LiDAR for Traffic and Safety Studies—Object Detection and Tracking

The ability to accurately measure and cost-effectively collect traffic data at road intersections is needed to improve their safety and operations. This study investigates the feasibility of using laser ranging technology (LiDAR) for this purpose. The proposed technology does not experience some of the problems of the current video-based technology but less expensive low-end sensors have limited density of points where measurements are collected that may bring new challenges. A novel LiDAR-based portable traffic scanner (TScan) is introduced in this report to detect and track various types of road users (e.g., trucks, cars, pedestrians, and bicycles). The scope of this study included the development of a signal processing algorithm and a user interface, their implementation on a TScan research unit, and evaluation of the unit performance to confirm its practicality for safety and traffic engineering applications. The TScan research unit was developed by integrating a Velodyne HDL-64E laser scanner within the existing Purdue University Mobile Traffic Laboratory which has a telescoping mast, video cameras, a computer, and an internal communications network. The low-end LiDAR sensor’s limited resolution of data points was further reduced by the distance, the light beam absorption on dark objects, and the reflection away from the sensor on oblique surfaces. The motion of the LiDAR sensor located at the top of the mast caused by wind and passing vehicles was accounted for with the readings from an inertial sensor atop the LiDAR. These challenges increased the need for an effective signal processing method to extract the maximum useful information. The developed TScan method identifies and extracts the background with a method applied in both the spherical and orthogonal coordinates. The moving objects are detected by clustering them; then the data points are tracked, first as clusters and then as rectangles fit to these clusters. After tracking, the individual moving objects are classified in categories, such as heavy and non-heavy vehicles, bicycles, and pedestrians. The resulting trajectories of the moving objects are stored for future processing with engineering applications. The developed signal-processing algorithm is supplemented with a convenient user interface for setting and running and inspecting the results during and after the data collection. In addition, one engineering application was developed in this study for counting moving objects at intersections. Another existing application, the Surrogate Safety Analysis Model (SSAM), was interfaced with the TScan method to allow extracting traffic conflicts and collisions from the TScan results. A user manual also was developed to explain the operation of the system and the application of the two engineering applications. Experimentation with the computational load and execution speed of the algorithm implemented on the MATLAB platform indicated that the use of a standard GPU for processing would permit real-time running of the algorithms during data collection. Thus, the post-processing phase of this method is less time consuming and more practical. Evaluation of the TScan performance was evaluated by comparing to the best available method: video frame-by-frame analysis with human observers. The results comparison included counting moving objects; estimating the positions of the objects, their speed, and direction of travel; and counting interactions between moving objects. The evaluation indicated that the benchmark method measured the vehicle positions and speeds at the accuracy comparable to the TScan performance. It was concluded that the TScan performance is sufficient for measuring traffic volumes, speeds, classifications, and traffic conflicts. The traffic interactions extracted by SSAM required automatic post-processing to eliminate vehicle interactions at too low speed and between pedestrians – events that could not be recognized by SSAM. It should be stressed that this post processing does not require human involvement. Nighttime conditions, light rain, and fog did not reduce the quality of the results. Several improvements of this new method are recommended and discussed in this report. The recommendations include implementing two TScan units at large intersections and adding the ability to collect traffic signal indications during data collection

Improving the Geotagging Accuracy of Street-level Images

Integrating images taken at street-level with satellite imagery is becoming increasingly valuable in the decision-making processes not only for individuals, but also in business and governmental sectors. To perform this integration, images taken at street-level need to be accurately georeferenced. This georeference information can be derived from a global positioning system (GPS). However, GPS data is prone to errors up to 15 meters, and needs to be corrected for the purpose of geo-referencing. In this thesis, an automatic method is proposed for correcting the georeference information obtained from the GPS data, based on image registration techniques. The proposed method uses an optimization technique to find local optimal solutions by matching high-level features and their relative locations. A global optimization method is then employed over all of the local solutions by applying a geometric constraint. The main contribution of this thesis is introducing a new direction for correcting the GPS data which is more economical and more consistent compared to existing manual method. Other than high cost (labor and management), the main concern with manual correction is the low degree of consistency between different human operators. Our proposed automatic software-based method is a solution for these drawbacks. Other contributions can be listed as (1) modified Chamfer matching (CM) cost function which improves the accuracy of standard CM for images with various misleading/disturbing edges; (2) Monte-Carlo-inspired statistical analysis which made it possible to quantify the overall performance of the proposed algorithm; (3) Novel similarity measure for applying normalized cross correlation (NCC) technique on multi-level thresholded images, which is used to compare multi-modal images more accurately as compared to standard application of NCC on raw images. (4) Casting the problem of selecting an optimal global solution among set of local minima into a problem of finding an optimal path in a graph using Dijkstra\u27s algorithm. We used our algorithm for correcting the georeference information of 20 chains containing more than 7000 fisheye images and our experimental results show that the proposed algorithm can achieve an average error of 2 meters, which is acceptable for most of applications