Automatic vehicle tracking and recognition from aerial image sequences

TL;DR

This paper presents a method for automated vehicle tracking and recognition from aerial images, utilizing linear appearance subspaces, with promising real-world experimental results and discussions on future improvements.

Contribution

It introduces a practical system combining data extraction, normalization, and subspace modeling for vehicle recognition from aerial sequences.

Findings

High correct recognition rate achieved

Few meaningful errors observed

Effective data normalization process

Abstract

This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences. Motivated by its successes in the existing literature focus on the use of linear appearance subspaces to describe multi-view object appearance and highlight the challenges involved in their application as a part of a practical system. A working solution which includes steps for data extraction and normalization is described. In experiments on real-world data the proposed methodology achieved promising results with a high correct recognition rate and few, meaningful errors (type II errors whereby genuinely similar targets are sometimes being confused with one another). Directions for future research and possible improvements of the proposed method are discussed.