Multi-view Vehicle Type Recognition with Feedback-enhancement Multi-branch CNNs
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Abstract
Vehicle type recognition (VTR) is a quite common requirement and one of key challenges in real surveillance scenarios such as intelligent traffic and unmanned driving. Usually coarse-grained VTR and fine-grained VTR are applied in different applications, and the challenge from multiple viewpoints is critical for both cases. In this paper, we propose a Feedback- enhancement Multi-branch CNN (FM-CNN) to solve the chal- lenge in these two cases. The proposed FM-CNN takes three derivatives of an image as input and leverages the advantages of hierarchical details, feedback enhancement, model average and stronger robustness to translation and mirroring. A single global cross-entropy loss is insufficient to train such a complex CNN and so we add extra branch losses to enhance feedbacks of each branch. Though reusing pre-trained parameters, we propose a novel parameter update method to adapt FM-CNN to task-specific local visual patterns and global information in new datasets. To test the effectiveness of FM-CNN, we create our own Multi-view VTR (MVVTR) dataset since there is no such datasets available. And for fine-grained VTR, we use CompCars dataset. Compared with state-of-the-art solutions, the proposed FM-CNN demonstrates better performance in both coarse-grained and fine- grained scenarios. For coarse-grained VTR, it achieves 94.9% Top-1 accuracy on MVVTR dataset. For fine-grained VTR, it achieves 91.0% Top-1 and 97.8% Top-5 accuracies on CompCars dataset.
Paper
to be published.
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