Colored-Noise Tracking of Floating Objects using UAVs with Thermal Cameras

Helgesen, Håkon Hagen; Leira, Frederik Stendahl; Johansen, Tor Arne

doi:10.1109/icuas.2019.8797931

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Non-human object tracking from drones using a LWIR band thermal camera can be found in [16,17]. In [16], a boat is captured and tracked with the Kalman filter and a constant velocity motion model.…”

Section: Introductionmentioning

confidence: 99%

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Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Yeom

2021

Drones

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Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video.

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Section: Introductionmentioning

confidence: 99%

“…In [16], a boat is captured and tracked with the Kalman filter and a constant velocity motion model. A colored-noise measurement model was adopted to track a small vessel [17]. However, in [16,17], a fixed-wing drone capable of maintaining stable flight was used at sea, which is high detection and low false alarm environment.…”

Section: Introductionmentioning

confidence: 99%

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Yeom

2021

Drones

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“…More complex target state transition models are presented in [24]. Without prior knowledge about the target behavior, a simple model with a small amount of parameters is often the most reliable choice [14].…”

Section: Target State-transition Modelmentioning

confidence: 99%

“…The measurement model depends on the sensor, but a recurrent requirement is the need for accurate pose information (sensor position and orientation). This is particularly challenging when the sensor is mounted on an agile platform, such as fixed-wing unmanned aerial vehicles (UAVs) [12,14].…”

Section: Introductionmentioning

confidence: 99%

Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

Helgesen

Bryne

Wilthil

et al. 2021

J Intell Robot Syst

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This article concerns tracking of floating objects using fixed-wing UAVs with a monocular thermal camera. Target tracking from an agile aerial vehicle is challenging because uncertainty in the UAV pose negatively affects the accuracy of the measurements obtained through thermal images. Consequently, the accuracy of the tracking estimates is degraded if navigation uncertainty is neglected. This is especially relevant for the estimated target covariance since inconsistency is a likely consequence. A tracking system based on the Schmidt-Kalman filter is proposed to mitigate navigation uncertainty. Images gathered with an uncertain UAV pose are weighted less than images captured with a reliable pose. The UAV pose is estimated independently in a multiplicative extended Kalman filter where the estimated covariance matrix is a measure of the uncertainty. The method is compared experimentally with two traditional alternatives based on the extended Kalman filter. The results show that the proposed method performs better with respect to consistency and accuracy.

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“…A boat was captured and tracked with the Kalman fi wing drone in [19]. A small vessel was tracked by adopting a colored-noise model in [20]. In the nonvisual approach, high-cost sensors add more p drone, or infrastructure is required on the ground or in a vehicle.…”

Section: Introductionmentioning

confidence: 99%

Long Distance Ground Target Tracking with Aerial Image-to-Position Conversion and Improved Track Association

Yeom

2022

Drones

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A small drone is capable of capturing distant objects at a low cost. In this paper, long distance (up to 1 km) ground target tracking with a small drone is addressed for oblique aerial images, and two novel approaches are developed. First, the coordinates of the image are converted to real-world based on the angular field of view, tilt angle, and altitude of the camera. Through the image-to-position conversion, the threshold of the actual object size and the center position of the detected object in real-world coordinates are obtained. Second, the track-to-track association is improved by adopting the nearest neighbor association rule to select the fittest track among multiple tracks in a dense track environment. Moving object detection consists of frame-to-frame subtraction and thresholding, morphological operation, and false alarm removal based on object size and shape properties. Tracks are initialized by differencing between the two nearest points in consecutive frames. The measurement statistically nearest to the state prediction updates the target’s state. With the improved track-to-track association, the fittest track is selected in the track validation region, and the direction of the displacement vector and velocity vectors of the two tracks are tested with an angular threshold. In the experiment, a drone hovered at an altitude of 400 m capturing video for about 10 s. The camera was tilted 30° downward from the horizontal. Total track life (TTL) and mean track life (MTL) were obtained for 86 targets within approximately 1 km of the drone. The interacting multiple mode (IMM)-CV and IMM-CA schemes were adopted with varying angular thresholds. The average TTL and MTL were obtained as 84.9–91.0% and 65.6–78.2%, respectively. The number of missing targets was 3–5; the average TTL and MTL were 89.2–94.3% and 69.7–81.0% excluding the missing targets.

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Colored-Noise Tracking of Floating Objects using UAVs with Thermal Cameras

Cited by 6 publications

References 27 publications

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

Long Distance Ground Target Tracking with Aerial Image-to-Position Conversion and Improved Track Association

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