- freely available
Sensors 2016, 16(1), 97; https://doi.org/10.3390/s16010097
2. Experimental Design
2.1. System Architecture
2.1.1. Unmanned Aerial Vehicle (UAV)
Power and Propulsion
FLIR Camera, Gimbal System and Video Transmission
Remote Display for Visualization
2.2. Algorithms for Counting and Tracking
2.2.1. Algorithm 1: Pixel Intensity Threshold (PIT)
2.2.2. Algorithm 2: Template Matching Binary Mask (TMBM)
- Load templates: In this first step template images are selected (e.g., koala, kangaroo, deer, pigs or birds). The templates are taken from the original footage or a database of images and then saved as small images of the object of interest. The algorithm is able to search for multiple templates in each frame.
- Processes Templates: The contrast of the template is increased in order to enhance the possibility of finding this template in the footage; white is made lighter and black darker by adding or subtracting a constant, C, from each pixel value, p, depending on a threshold, T.
- Search for each template in the video frame: For a detected animal to be recorded as a match it must be able to pass a scoring threshold. The searching function returns a score from 1 to 10, based on the proximity of the template to the matched object, where 1 indicates the smallest chance of finding the target and 10 indicates a perfect match. A score of 7 was designed to reflect a high quality match that gave an acceptable chance of avoiding false positives. In addition, to avoid false positives, any match found has to be present for at least 10 consecutive frames before it is considered to be a true match.
- Assign coordinates: once a match has been found, the pixel coordinates (x, y) of the location within the frame are stored for later use.
- Create a mask using coordinates: A new image is created with the same dimension as the source footage with a black (pixel with a value of 0) background. Using the coordinates within the image of the match found in the previous step, a white (pixel with a value of 255) bounding box or circle is drawn with a variable area (Figure 11b). The size of this area is defined by calculating the area of the match. The mask image aims to reduce the search area, eliminating what is considered as background.
- Logical operation with the mask: In this step a logical AND is applied using the current frame and the mask. As a result the background is eliminated leaving only the regions of interest at the front.
- Pixel intensity threshold: In this step the function described in Step 2 is used to assign a 0 if the pixel value is less than or equal to the threshold and 255 otherwise.
- Tracking: After obtaining an image comprising only the objects, a function to track is implemented. This function is capable of identifying multiple objects within the same frame and can also distinguish one from another by using their coordinates. The coordinates of the current objects are compared to the previous mask obtained, therefore making it possible to recognize if an object of interest has moved.
- Counting: This function is able to number, count and display matches in the current frame and throughout the video. This is established by using the object’s coordinates within the frame to differentiate multiples objects and to count the number of consecutive frames in which those objects have appeared. If this number increases, it means the object is still in the frame and this is counted as a match. If the object leaves the scene for a specified number of frames after being identified as a match, it is included in the total count.
- Last frame Loop: In this last step the algorithm then checks if the current frame is the last. If not the algorithm restarts at Step 3; otherwise the process ends.
3. Validation Test
3.1. Focal Species: Koala
3.2. Study Area
3.3. Data Acquisition
|Mobius RGB Camera||FLIR Thermal Camera|
|Size||61 mm × 35 mm × 18 mm||44.5 mm × 44.5 mm × 55 mm|
|Weight||38 g||72 g|
|Spectrum Wavelength||Visible RGB||7.5 -13.5 µm|
|Resolution||1080 p||640 × 510|
|Focal Length||2.1 mm||25 mm|
|Frame Rate||30 fps||9 fps|
4. Results and Discussion
- If the target wildlife (e.g., koala) is found in at least 10 consecutive frames it is counted as a match
- The target wildlife (e.g., koala) that has been identified cannot make big jumps in location (coordinates) between consecutive frames.
- A horizontal displacement of the target wildlife (e.g., koala) is expected to be within the circle surrounding the target in the mask
- The size of the target wildlife (e.g., koala) cannot suddenly increase drastically (area in pixels).
- If the target wildlife (e.g., koala) being tracked is not found for 10 consecutive frames it is considered lost or out of the frame.
|Altitude||Number of Detections||Actual Number of Koalas||Average Detection Time (s)||Average False Positives|
|60 m||5 to 6||6||2.1||1.5|
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