Robust and Fast Vehicle Detection using Augmented Confidence Map
This addresses vehicle detection for real-time applications such as autonomous driving or surveillance, but it appears incremental as it builds on existing techniques like CNN and MSER.
The paper tackles vehicle detection in real-time scenarios by proposing a method that generates an augmented confidence map using MR-MSER and fast CNN, combined with rough set and fuzzy-based models for robustness. It achieves improved time efficiency and a good detection rate on drone-captured data and benchmark datasets like KITTI and UA-DETRAC.
Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc. This paper presents a new method relied on generating a confidence map-for robust and faster vehicle detection. To reduce the adverse effect of different speeds, shapes, structures, and the presence of several vehicles in a single image, we introduce the concept of augmentation which highlights the region of interest containing the vehicles. The augmented map is generated by exploring the combination of multiresolution analysis and maximally stable extremal regions (MR-MSER). The output of MR-MSER is supplied to fast CNN to generate a confidence map, which results in candidate regions. Furthermore, unlike existing models that implement complicated models for vehicle detection, we explore the combination of a rough set and fuzzy-based models for robust vehicle detection. To show the effectiveness of the proposed method, we conduct experiments on our dataset captured by drones and on several vehicle detection benchmark datasets, namely, KITTI and UA-DETRAC. The results on our dataset and the benchmark datasets show that the proposed method outperforms the existing methods in terms of time efficiency and achieves a good detection rate.