Joint acoustic-video fingerprinting of vehicles, part II

In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data

Target tracking using a joint acoustic video system

In this paper, we present a particle filter that exploits multi modal information for robust target tracking. We demonstrate a Bayesian framework for combining acoustic and video information using a state space approach. A proposal strategy for joint acoustic and video state-space tracking using particle filters is given by carefully placing the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint filter posterior estimate decreases the worst case divergence of the individual modalities. Hence, the joint tracking filter is robust against video and acoustic occlusions. We also introduce a time-delay variable to the joint state space to handle the acoustic-video data synchronization issue, caused by acoustic propagation delay. Computer simulations are presented with field and synthetic data to demonstrate the filter’s performance

An Approach to Counting Vehicles from Pre-Recorded Video Using Computer Algorithms

One of the fundamental sources of data for traffic analysis is vehicle counts, which can be conducted either by the traditional manual method or by automated means. Different agencies have guidelines for manual counting, but they are typically prepared for particular conditions. In the case of automated counting, different methods have been applied, but You Only Look Once (YOLO), a recently developed object detection model, presents new potential in automated vehicle counting. The first objective of this study was to formulate general guidelines for manual counting based on experience gained in the field. Another goal of this study was to develop a computer program for vehicle counting from pre-recorded video applying the YOLO model. The documented general guidelines provided in this project can be useful in acquiring the required standard and minimizing the cost of a manual counting project. The accuracy of the automated counting program was found to be about 90 percent for total daily counts, although most of that error was a consistent undercounting by automated counting