Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles

Detection and distance estimation of micro unmanned aerial vehicles (mUAVs) is crucial for (i) the detection of intruder mUAVs in protected environments; (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios, such as environmental monitoring, surveillance and exploration. In this article, we evaluate vision algorithms as alternatives for detection and distance estimation of mUAVs, since other sensing modalities entail certain limitations on the environment or on the distance. For this purpose, we test Haar-like features, histogram of gradients (HOG) and local binary patterns (LBP) using cascades of boosted classifiers. Cascaded boosted classifiers allow fast processing by performing detection tests at multiple stages, where only candidates passing earlier simple stages are processed at the preceding more complex stages. We also integrate a distance estimation method with our system utilizing geometric cues with support vector regressors. We evaluated each method on indoor and outdoor videos that are collected in a systematic way and also on videos having motion blur. Our experiments show that, using boosted cascaded classifiers with LBP, near real-time detection and distance estimation of mUAVs are possible in about 60 ms indoors (1032x778 resolution) and 150 ms outdoors (1280x720 resolution) per frame, with a detection rate of 0.96 F-score. However, the cascaded classifiers using Haar-like features lead to better distance estimation since they can position the bounding boxes on mUAVs more accurately. On the other hand, our time analysis yields that the cascaded classifiers using HOG train and run faster than the other algorithms.


Vision-based detection and distance estimation of micro unmanned aerial vehicles
Gökçe, Fatih; Üçoluk, Göktürk; Department of Computer Engineering (2015)
In this thesis, we study visual detection and distance estimation of Micro Unmanned Aerial Vehicles (mUAVs), a crucial problem for (i) intrusion detection of mUAVs in protected environments, (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios such as environmental monitoring, surveillance and exploration. The problem is challenging since (i) a real-time solution is required, a burden when computational power is limited by the hardware carried by an mUAV,...
Unmanned Aerial Vehicle Domain: Areas of Research
Demir, Kadir Alpaslan; Cicibas, Halil; ARICA, NAFİZ (2015-07-01)
Unmanned aerial vehicles (UAVs) domain has seen rapid developments in recent years. As the number of UAVs increases and as the missions involving UAVs vary, new research issues surface. An overview of the existing research areas in the UAV domain has been presented including the nature of the work categorised under different groups. These research areas are divided into two main streams: Technological and operational research areas. The research areas in technology are divided into onboard and ground techno...
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Skoglar, Per; Orguner, Umut; Tornqvist, David; Gustafsson, Fredrik (2012-07-01)
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decisio...
Nonlinear Guidance of Aircraft Formations
Tekinalp, Ozan (null; 2015-01-05)
Nonlinear formation flight control algorithm for a pair of unmanned aerial vehicles (UAV) is proposed. The leader-follower approach to formation flight is adopted. The leader maintains a prescribed trajectory while the follower is controlled to track and maintain a fixed relative distance from its leader. Two nonlinear guidance algorithms, Lyapunov and State Dependent Ricatti Equation, (SDRE) based are proposed for the relative guidance of the follower UAV. The resulting formation control systems are tested...
Citation Formats
F. Gökçe, G. Üçoluk, E. Şahin, and S. Kalkan, “Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles,” SENSORS, pp. 23805–23846, 2015, Accessed: 00, 2020. [Online]. Available: