Insects such as Diptera are capable of highly complex aerial maneuvers and rapid responses to environmental stimuli, making them a subject of interest for studies in flight dynamics and motor control. To accurately quantify these movements, high-speed cameras are employed, capturing footage at 5000 frames per second. This results in a vast amount of data, necessitating the development of automated analysis models. In this work, we present a segmentation model specifically designed for the wings of Diptera species, aiming to track their wingbeats with high precision. The model’s performance was evaluated on species included in the training set as well as on species only included in the testset, allowing us to assess its generalization capabilities. Our results show that the model achieves a 95th percentile error of 7.8 pixels, while operating at a speed 2500 times faster than manual human analysis. This significant improvement in speed highlights the potential for our model to facilitate large-scale, high-speed analysis of insect flight, with implications for both biological research and bio-inspired engineering applications.

Tracker-level fusion (TLF) is recognized as an effective approach to comprehensively improve visual object tracking performance by combining the capabilities of multiple baseline trackers. Although there is considerable interest in TLF, there are still challenges related to insufficient understanding, high cost, and unstable performance that make studying TLF difficult. In this thesis, I begin with an explicit summary of the overall pipeline of TLF which has significant guidance for TLF study. Additionally, I conduct a deep analysis of the positive and negative effects of baseline trackers to fully understand their influence on TLF. For visual object tracking, I propose three TLF frameworks based on three Distributed Kalman filters which are optimized for different scenarios and enable the fusion of different baseline trackers to enhance tracking performance. My TLF frameworks fuse the tracking results of different baseline trackers based on the principle of minimal trace to produce fusion results.Additionally, they exhibit superior and stable performance with general baseline tracker requirements, while also being simple, online, and real-time.Furthermore, the proposed frameworks can benefit from state-of-the-art baseline trackers over time, which will further improve their tracking performance. The proposed analysis and frameworks are studied extensively on three challenging benchmarks: generic tracking OTB2015, short-term tracking GOT-10k, and long-term tracking LaSOT. At over 240 FPS, the state-of-the-art success AUC score of 72.7% is achieved on OTB2015.