Detection and recovery from camera bump during automated inspection of automobiles on an assembly line

This thesis details the steps taken to detect and compensate for camera bumps while per- forming part identification using VI at the BMW manufacturing plant and on the simulation testbed. For the system presented here to work, the user is required to record a video from the camera before the camera is bumped and one after the camera has been bumped. The premise behind the method suggested here is that the transformation between the background in the pre- and post-bump video will be equal to the transformation in the foreground. A Background extraction program is used to generate a background image from the pre- and post-bump videos. Feature tracking and matching is performed on the background images to find the transformation between them. This transfor- mation is then applied to the templates extracted from the pre-bump video. An additional manual compensation step is needed in cases where the transformation in the background is not equal to the transformation in the foreground. The resultant transformation is applied to all the templates of the pre-bump video and VI is seen to successfully identify parts with sufficient accuracy

Fast Full-frame Video Stabilization with Iterative Optimization

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.Comment: Accepted by ICCV202

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both.This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1140 minutes of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multi-video stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post-encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations' three-dimensional base maps and signal timings.Comment: Transportation Research Record (2023

Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles

Abstract This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data

Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms