Correlation filter based visual trackers for person pursuit using a low-cost Quadrotor

Haag, K.; Dotenco, Sergiu; Gallwitz, Florian

doi:10.1109/i4cs.2015.7294481

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…In [Lin et al, 2012] a predefined color of the object is used to distinguish the object from the background, followed by template matching to perform detection. In [Haag et al, 2015], [Pestana et al, 2014] a feature-based tracker is used to follow the person with a UAV. Here the person should first be manually selected (using a bounding box) in the life video feed before the tracker can start.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Flying Cameraman with Embedded Person Detection and Tracking while Applying Cinematographic Rules

Hulens

Goedemé

2017

2017 14th Conference on Computer and Robot Vision (CRV)

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Unmanned Aerial Vehicles (UAVs) enable numerous applications such as search and rescue operations, structural inspection of buildings, crop growth analysis in agriculture, performing 3D reconstruction and so on. For such applications, currently the UAV is steered manually. However, in this paper we aim to record semi-professional video footage (e.g. concerts, sport events) using fully autonomous UAVs. Evidently, this is challenging since we need to detect and track the actor on-board a UAV in real-time, while automaticallyand smoothly -controlling the UAV based on these detections. For this, all four DOF (Degrees of freedom) are controlled in separate simultaneous control loops by our vision-based algorithms. Furthermore cinematographic rules need to be taken into account (e.g. the rule of thirds) which position the actor at the visually optimal location in the frame. We extensively validated our algorithms: each control loop and the overall final system is thoroughly evaluated with respect to both accuracy and control speed. We show that our system is able to efficiently control the UAV such that professional recordings are obtained.

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Section: Related Workmentioning

confidence: 99%

Autonomous Flying Cameraman with Embedded Person Detection and Tracking while Applying Cinematographic Rules

Hulens

Goedemé

2017

2017 14th Conference on Computer and Robot Vision (CRV)

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“…While adaptive data fusion has been well studied and established for multi-sensor measurements in general [19], researchers in the computer vision community have gone towards machine learning techniques to incorporate multiple image characteristics into tracking algorithms. Methods such as PROST [20], VTD [21], CMT [22], Struck [23], or the well known TLD [24] and its variants [25], [26] fit this framework. However, the aforementioned algorithms provide limited mechanisms to incorporate multiple and complementary feature extraction methods, thereby restricting their practical applicability.…”

Section: Related Workmentioning

confidence: 99%

“…Both are capable of following a target. While similar approaches have used vision-based trackers to control a small UAV in [31], [32] and [26]. Previous works did not consider the fusion of several methods at a bounding box level to improve reliability over longer time spans.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Bayesian Data Fusion for Robotic Platform Navigation

Echeverri,

Medeiros,

Walsh

et al. 2017

Preprint

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Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision field has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. Within this work, a hierarchical Bayesian fusion approach is proposed, which outperforms individual trackers by using redundant measurements. The adaptive framework is achieved by relying on each measurement's local statistics and a global softened majority voting. The proposed approach was validated in a simulated application and two robotic platforms.

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“…Vison-based target tracking with UAVs has been researched in [15][16][17][18][19][20][21][22]. Correlation filtering-based tracking was studied in [15].…”

Section: Introductionmentioning

confidence: 99%

“…Closely located-object tracking was performed with feature matching and multi-inertial sensing data in [16]. Pedestrians were tracked by template-matching in [17,18]. Small animals were tracked with a freely moving camera in [19].…”

Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Multiple Moving Vehicles with a UAV

Lee¹,

Yeom²

2018

IJFIS

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Multiple object detection and tracking are essential for video surveillance. A drone or unmanned aerial vehicle (UAV) is very useful for aerial surveillance due to the efficiency of capturing remote scenes. This paper addresses the detection and tracking of moving vehicles with a UAV. A UAV can capture video sequences above the road at a distance. The detection step consists of frame differencing followed by thresholding, morphological filtering, and removing false alarms considering the true size of vehicles. The centroids of the detected areas are considered as measurements for tracking. Tracking is performed with Kalman filtering to estimate the state of the target based on the dynamic state model and measured positions. In the experiment, three moving cars are captured at a long distance by a drone. Experimental results show that the proposed method well detects moving cars and achieves good accuracy in tracking their dynamic state.

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Correlation filter based visual trackers for person pursuit using a low-cost Quadrotor

Cited by 14 publications

References 18 publications

Autonomous Flying Cameraman with Embedded Person Detection and Tracking while Applying Cinematographic Rules

Autonomous Flying Cameraman with Embedded Person Detection and Tracking while Applying Cinematographic Rules

Hierarchical Bayesian Data Fusion for Robotic Platform Navigation

Detection and Tracking of Multiple Moving Vehicles with a UAV

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