Automatic vehicle tracking and recognition from aerial image sequences
This addresses vehicle tracking and recognition for aerial surveillance applications, but it is incremental as it builds on existing linear appearance subspace methods.
The paper tackled automated vehicle tracking and recognition from aerial image sequences by using linear appearance subspaces for multi-view object appearance, achieving a high correct recognition rate with few meaningful errors in experiments on real-world data.
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.