Vehicle detection with sub-class training using R-CNN for the UA-DETRAC benchmark
| Autor: | Sitapa Rujikietgumjorn, Nattachai Watcharapinchai |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Class (computer programming)
Computer science business.industry Feature vector Feature extraction Training (meteorology) 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Object detection Vehicle detection 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer 0105 earth and related environmental sciences |
| Zdroj: | AVSS |
| DOI: | 10.1109/avss.2017.8078520 |
| Popis: | Different types of vehicles, such as buses and cars, can be quite different in shapes and details. This makes it more difficult to try to learn a single feature vector that can detect all types of vehicles using a single object class. We proposed an approach to perform vehicle detection with Sub-Classes categories learning using R-CNN in order to improve the performance of vehicle detection. Instead of using a single object class, which is a vehicle in this experiment, to train on the R-CNN, we used multiple sub-classes of vehicles so that the network can better learn the features of each individual type. In the experiment, we also evaluated the result of using a transfer learning approach to use a pre-trained weights on a new dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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