<p dir="ltr">Agricultural applications often require human experts with domain knowledge to ensure compliance and improve productivity, which can be costly and inefficient. To tackle this problem, automated video systems can be implemented for agricultural tasks thanks to the ubiquity of cameras. In this thesis, we focus on designing and implementing video analytics systems for real applications in agriculture by combining both traditional image processing and recent advancements in computer vision. Existing research and available methods have been heavily focused on obtaining the best performance on large-scale benchmarking datasets, while neglecting the applications to real-world problems. Our goal is to bridge the gap between state-of-art methods and real agricultural applications. More specifically, we design video systems for the two tasks of monitoring turkey behavior for turkey welfare and handwashing action recognition for improved food safety. For monitoring turkeys, we implement a turkey detector, a turkey tracker, and a turkey head tracker by combining object detection and multi-object tracking. Furthermore, we detect turkey activities by incorporating motion information. For recognizing handwashing activities, we combine a hand extraction method for focusing on the hand regions with a neural network to build a hand image classifier. In addition, we apply a two-stream network with RGB and hand streams to further improve performance and robustness.</p><p dir="ltr">Besides designing a robust hand classifier, we explore how dataset attributes and distribution shifts can impact system performance. In particular, distribution shifts caused by changes in hand poses and shadow can cause a classifier’s performance to degrade sharply or breakdown beyond a certain point. To better explore the impact of hand poses and shadow and to mitigate the induced breakdown points, we generate synthetic data with desired variations to introduce controlled distribution shift. Experimental results show that the breakdown points are heavily impacted by pose and shadow conditions. In addition, we demonstrate mitigation strategies to significant performance degradation by using selective additional training data and adding synthetic shadow to images. By incorporating domain knowledge and understanding the applications, we can effectively design video analytics systems and apply advanced techniques in agricultural scenarios.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/26339236 |
| Date | 20 July 2024 |
| Creators | Shengtai Ju (19180429) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Video_Analytics_for_Agricultural_Applications/26339236 |
Page generated in 0.0015 seconds