Revisiting Stereo Triangulation in UAV Distance Estimation

Distance estimation plays an important role for path planning and collision avoidance of swarm UAVs. However, the lack of annotated data seriously hinders the related studies. In this work, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the stereo triangulation cannot stand for UAV scenes. The core reason is the position deviation issue due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module, which can directly predict the offset between the observed positions and the actual ones and then perform compensation in stereo triangulation calculation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. We conduct extensive experiments on UAVDE, and our method can achieve a significant performance improvement over a strong baseline (by reducing the relative difference from 49.4% to 9.8%), which demonstrates its effectiveness and superiority. The code and dataset are available at https://github.com/duanyuan13/PCM.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Efficient License Plate Recognition via Holistic Position Attention

License plate recognition (LPR) is a fundamental component of various intelligent transportation systems, and is always expected to be accurate and efficient enough in real-world applications. Nowadays, recognition of single character has been sophisticated benefiting from the power of deep learning, and extracting position information for forming a character sequence becomes the main bottleneck of LPR. To tackle this issue, we propose a novel holistic position attention (HPA) in this paper that consists of position network and shared classifier. Specifically, the position network explicitly encodes the character position into the maps of HPA, and then the shared classifier performs the character recognition in a unified and parallel way. Here the extracted features are modulated by the attention maps before feeding into the classifier to yield the final recognition results. Note that our proposed method is end-to-end trainable, character recognition can be concurrently performed, and no post-processing is needed. Thus our LPR system can achieve good effectiveness and efficiency simultaneously. The experimental results on four public datasets, including AOLP, Media Lab, CCPD, and CLPD, well demonstrate the superiority of our method to previous state-of-the-art methods in both accuracy and speed

Exploit Domain-Robust Optical Flow in Domain Adaptive Video Semantic Segmentation

Domain adaptive semantic segmentation aims to exploit the pixel-level annotated samples on source domain to assist the segmentation of unlabeled samples on target domain. For such a task, the key is to construct reliable supervision signals on target domain. However, existing methods can only provide unreliable supervision signals constructed by segmentation model (SegNet) that are generally domain-sensitive. In this work, we try to find a domain-robust clue to construct more reliable supervision signals. Particularly, we experimentally observe the domain-robustness of optical flow in video tasks as it mainly represents the motion characteristics of scenes. However, optical flow cannot be directly used as supervision signals of semantic segmentation since both of them essentially represent different information. To tackle this issue, we first propose a novel Segmentation-to-Flow Module (SFM) that converts semantic segmentation maps to optical flows, named the segmentation-based flow (SF), and then propose a Segmentation-based Flow Consistency (SFC) method to impose consistency between SF and optical flow, which can implicitly supervise the training of segmentation model. The extensive experiments on two challenging benchmarks demonstrate the effectiveness of our method, and it outperforms previous state-of-the-art methods with considerable performance improvement. Our code is available at https://github.com/EdenHazardan/SFC