## 2 Related Work  
Several investigations on video-based CEBM are related to our research. In this section, we briefly review these investigations and outline the contributions reported in this article. [PERSON] et al. [11] present a color segmentation algorithm to track bees pollinating macadamia trees. The algorithm computes the absolute differences of the Minkowsky metric [12] between the R-, G-, and B-channels of consecutive frames. The researchers report that the Minkowsky metric shows greater differences when frames contain bees flying against clear, static background and smaller values with frames that contain bees flying behind flowers. The channel differences are used to detect flying bees. The researchers claim that their algorithm can detect motions of leaves and flowers. The proposed method does not distinguish small stigmas or vibrating leaves from bees or bees flying over flowers. [PERSON] et al. [13] propose a vector quantization method to segment bee bodies in video frames. The methods identifies bee body regions with vector quantization. Two types of regions, named single honeybee region (SHR) and plural honeybee region (PHR), are extracted with morphological information such as body size and body shape. The method works on 30 frames per second (fps) videos with a resolution of 720 \(\times\) 480 and is reported to detect over 500 bees per frame. In the reported experiments, the method successfully identified 72% of the bees. The proposed method appears to work best to track motions in large masses of bees inside the hive, not outside of it. [PERSON] et al. [14] propose a computer vision system to monitor the beehive's in-and-out activity. The system uses an infrared LED camera, an infrared LED light source, and a personal computer to process images. A large sample of forager bees is captured and placed in a freezer where the bees are rendered temporarily motionless due to low temperature. A circular character encoding tag is glued to each motionless bee's thorax. The tags are used to recognize the bees' flight orientations in images. Hough transform [15] is used to detect tags and classify them with an SVM. The researchers report the orientation classification accuracy of \(\approx\)98% and claim that their method avoids the disadvantages of heavier RFID tags and does not require external readers emitting electromagnetic waves. Placing RFID tags on bees interferes with natural life cycles of bees and is not practical for commercial apiarists. [PERSON] [16] proposes a video bee traffic monitoring system whose camera is placed in front of or on top of a hive. The system estimates bee traffic by detecting changes between the current image and the average image of the preceding eleven images. The change mask contains segments, each of which is assumed to correspond to a single bee. The sum of these segments constitutes a bee count. The reported research appears to offer insufficient detail on the accuracy of the proposed method. [PERSON] et al. [17] proposed a system for detecting pollen bearing honeybee in videos at the entrance of a hive. The system's raspberry pi camera is placed in a box above the hive entrance. A glass plate is placed on the bottom side of the box, 2 cm above the landing pad. This configuration forces the bees entering or leaving the hive to crawl \(\approx\)11 cm. The training dataset consisted of 50 images of pollen bearing honeybees and 50 images of honeybees without pollen. The test dataset consisted of 354 honeybee images. Since the bees in the camera's field of view cannot fly, the system's design may interfere with natural bee cycles. The experimental results are not replicable insomuch as the test datasets do not appear to be public. [PERSON] et al. [18] designed a system for pose detection and tracking of insects and animals. The researchers used their system to monitor the traffic of honeybees and mice. A deep neural network was trained to detect and associate body parts into whole insects or animals.