Aiming at the occlusion problem in the target tracking algorithm FDSST (Fast Discriminative Scale Space Tracking), this paper designs an improved SSDA (Sequential Similarity Detection Algorithms)-based FDSST anti-occlusion algorithm. The algorithm mainly improves the model update strategy of FDSST, the main processes are as follows: Firstly, judge whether the target has occlusion according to the oscillation degree of the correlation filter response graph; then, when there is occlusion, the SSDA algorithm is applied to re-detect the target according to the search strategy, to restore the occluded target and re-track the target. Our method was transplanted to onboard Jetson TX2, and the actual test was carried out on the public dataset OTB50 and the aerial video dataset respectively. The experimental results show that the proposed algorithm retains the advantages of FDSST and improves its anti-occlusion ability effectively.
Word embedding have been used in numerous Natural Language Processing and Machine Learning tasks. However, it is a high-dimensional vector field that propagate stereotypes to software applications. Its current debiasing frameworks do not completely capture its embedded patterns. In this paper, we propose deb2viz, a visual debiasing approach that explores and manipulates the high-dimensional patterns of word embedding field. First, we partition this vector field into interrelated low-dimensional subspaces to equalize and neutralize distances between gender-definitional and gender-neutral words. To further reduce gender bias, we update the distances of appropriate nearest neighbors for gender-neutral words to be arbitrarily close. Experimental results on several benchmark standards show the competitiveness of our proposed method in mitigating bias within pre-trained word2vec embedding model.
Research on characteristics of helicopter rotor blade tip vortex (BTV) is one of the key elements for helicopter rotor aerodynamic characteristics research. The existing traditional computational fluid dynamics (CFD) based detection methods of vortex core area in the flow field mainly use points or lines in the flow field for calculation. However, the traditional CFD-based model is complex, huge computational cost and without effective vortex core model. So the manual analysis will be necessary in some scenarios to simplify the work of vortex detection, such as vortex region detection for helicopter rotor BTV in domestic. In order to decrease the workload of manual analysis, we draw on the advanced research results in the field of computer vision and machine learning, especially target detection, and firstly propose a vortex region detection method in blade tip vortex based on You Only Look Once (YOLO) network. First of all, the vortex region is marked in flow field images under the guidance of domain experts to construct the vortex data set. Secondly, we propose an improved model based on yolo v3-tiny. Finally, the self-built vortex data set is used to train models. Experiments show that the CNN-based method has better result than traditional methods.
Outdoor target tracking UAV (Unmanned Aerial Vehicle), which is a research hotspot in the field of computer vision and unmanned aerial system, needs robust target-tracking algorithms with good real-time performance, accurate position estimator of UAV and the corresponding control strategy of the system. In this paper, we designed an outdoor drone tracking system using PCA (Principal Component Analysis) face recognition algorithm and KCF (Kernel Correlation Filter) target tracking algorithm. Firstly, an image acquisition unit is constructed by using an on-board pan-and-tilt camera to capture an outdoor monitored area. Secondly, the PCA algorithm is used for face matching, then the tracking mode is automatically transferred when the expected face target is recognized. Finally, the target tracking is performed by the KCF algorithm. After that, the position error is calculated and sent to the flight control system through the MavLink protocol, thereby performing posture adjustment and completing the tracking and monitoring task. Experimental results show that the performance of outdoor target tracking flight robot is stable and reliable, which meets the requirements of outdoor target tracking and has a good application prospect.
Pedestrian detection (PD) is an important application domain in computer vision and pattern recognition. Conventional PD in real life scene is usually based on a fixed camera, which can detect and track the pedestrians in the monitoring region. However, when the pedestrian leaves the visible area of the fixed camera, it is usually difficult, if not impossible, to monitor the pedestrian. In response to the limitations of the conventional pedestrian detection application scenarios, a four-rotor unmanned aerial vehicle (UAV) system equipped with a high-definition (HD) camera is designed and implemented to detect human targets. Considering the size of human body in aerial image is small and easily to be occluded, we draw on the advanced research results in the field of target detection and propose a robust pedestrian detection method based on YOLO (You Only Look Once) network. The flow of the proposed approach is as follows. Firstly, the HD camera, which is installed on the monitoring UAV, is used for capturing images of the designated outdoor area. Secondly, image sequences are collected and processed using the airborne embedded NVIDIA Jason TX1 and Ubuntu as the core and operating system, respectively. Finally, YOLO is used to train the pedestrian classifier and perform the pedestrian detection. Experimental results show that our method has good detection results under the complicated conditions of detecting small-scale pedestrians and pedestrian occlusion.
Pedestrian detection (PD) is an important application domain in computer vision and pattern recognition. Unmanned Aerial Vehicles (UAVs) have become a major field of research in recent years. In this paper, an algorithm for a robust pedestrian detection method based on the combination of the infrared HOG (IR-HOG) feature and SVM is proposed for highly complex outdoor scenarios on the basis of airborne IR image sequences from UAV. The basic flow of our application operation is as follows. Firstly, the thermal infrared imager (TAU2-336), which was installed on our Outdoor Autonomous Searching (OAS) UAV, is used for taking pictures of the designated outdoor area. Secondly, image sequences collecting and processing were accomplished by using high-performance embedded system with Samsung ODROID-XU4 and Ubuntu as the core and operating system respectively, and IR-HOG features were extracted. Finally, the SVM is used to train the pedestrian classifier. Experiment show that, our method shows promising results under complex conditions including strong noise corruption, partial occlusion etc.