Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently

Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals

Following the growing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on object tracking using videos recorded from UAVs. However, tracking from UAV videos poses many challenges due to platform motion, including background clutter, occlusion, and illumination variation. This paper tackles these challenges by proposing a correlation filter-based tracker with feature fusion and saliency proposals. First, we integrate multiple feature types such as dimensionality-reduced color name (CN) and histograms of oriented gradient (HOG) features to improve the performance of correlation filters for UAV videos. Yet, a fused feature acting as a multivector descriptor cannot be directly used in prior correlation filters. Therefore, a fused feature correlation filter is proposed that can directly convolve with a multivector descriptor, in order to obtain a single-channel response that indicates the location of an object. Furthermore, we introduce saliency proposals as re-detector to reduce background interference caused by occlusion or any distracter. Finally, an adaptive template-update strategy according to saliency information is utilized to alleviate possible model drifts. Systematic comparative evaluations performed on two popular UAV datasets show the effectiveness of the proposed approach

Saliency-based cooperative landing of a multirotor aerial vehicle on an autonomous surface vehicle

This paper presents a method for vision-based landing of a multirotor unmanned aerial vehicle (UAV) on an autonomous surface vehicle (ASV) equipped with a helipad. The method includes a mechanism for helipad behavioural search when outside the UAV’s field of view, a learning saliency-based mechanism for visual tracking the helipad, and a cooperative strategy for the final vision-based landing phase. Learning how to track the helipad from above occurs during takeoff and cooperation results from having the ASV tracking the UAV for assisting its landing. A set of experimental results with both simulated and physical robots show the feasibility of the presented method.info:eu-repo/semantics/acceptedVersio

PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

Sense and avoid using hybrid convolutional and recurrent neural networks

This work develops a Sense and Avoid strategy based on a deep learning approach to be used by UAVs using only one electro-optical camera to sense the environment. Hybrid Convolutional and Recurrent Neural Networks (CRNN) are used for object detection, classification and tracking whereas an Extended Kalman Filter (EKF) is considered for relative range estimation. Probabilistic conflict detection and geometric avoidance trajectory are considered for the last stage of this technique. The results show that the considered deep learning approach can work faster than other state-of-the-art computer vision methods. They also show that the collision can be successfully avoided considering design parameters that can be adjusted to adapt to different scenarios

Teleoperated visual inspection and surveillance with unmanned ground and aerial vehicles,” Int

Abstract—This paper introduces our robotic system named UGAV (Unmanned Ground-Air Vehicle) consisting of two semi-autonomous robot platforms, an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicles (UAV). The paper focuses on three topics of the inspection with the combined UGV and UAV: (A) teleoperated control by means of cell or smart phones with a new concept of automatic configuration of the smart phone based on a RKI-XML description of the vehicles control capabilities, (B) the camera and vision system with the focus to real time feature extraction e.g. for the tracking of the UAV and (C) the architecture and hardware of the UAV

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Aggregation signature for small object tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201