Simultaneous Automatic Tracking of Honeybee Dancers’ and Their Followers’ Walking Trajectories Using Image Processing
Shinya Takahashi, Koji Hashimoto , Sakashi Maeda , Naoyuki Tsuruta , Hiroyuki Ai , Shinya Takahashi
Fukuoka University, Japan; Fukuoka University, Japan ; Fukuoka University, Japan ; Fukuoka University, Japan ; Fukuoka University, Japan ; Fukuoka University, Japan
More than 50 years ago, Karl von Frisch discovered that honeybees (Apis mellifera) communicate the exact location of food sources to other bees through a complex movement called waggle dance. Since then, analyzing communications between the waggle dancers and their followers in their hive is one of the most important and interesting issues to reveal a mechanism of honeybee’s language. In general, these behavior analyses have been usually conducted by extracting honeybee’s walking trajectories from recorded long-time video data manually. Therefore, in order to decrease the hard work of observers and their artificial errors, we have previously proposed an automatic tracking algorithm of multiple honeybees using image processing (Takahashi et al., 2016). Besides, we have constructed an automatic recording system for long-term tracking of honeybee behaviors in an observation hive using several high-resolution camera modules connected to multiple small-size single board computers. Using this system, we recorded the hive and its corridor to the field from 6:30 am to 7:30 pm over 4 weeks in June 2016. The colony had about 800 honeybees including a queen. Finally, we obtained video data over 20TB per a month. Analyzing honeybee’s behavior from this enormous amount of data is required an extremely long time even if using a high spec computer. In order to deal with this issue, we first extracted the area and time of waggle dance from the recorded video data using a preprocessing based on frame-difference approach. Then we applied our tracking algorithm for the extracted partial video data. In the preliminary experiment, we conducted the automatic tracking of the waggle dancers and their followers for 13-hour video data and confirmed that our approach can detect their trajectories. In this poster presentation, we introduce our system and show some results obtained by our approach.