Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.

Event cameras are novel sensors whose high temporal resolution and bandwidth motivate their use for the optical flow estimation problem. However, the properties of event cameras also introduce a vulnerability to flickering. Flickering hurts the perceptibility of motion by overwhelming event data with unrelated information. The single existing event de-flicker method (EFR) is built for scenarios where the relative position of the camera and the flickering object is constant, which is uncommon in motion-heavy optical flow estimation scenarios. Our contribution is a new de-flickering method that incorporates spatial awareness of nearby pixels. We hypothesize this feature to increase robustness to movement, and thus to better improve optical flow accuracy. Compared to EFR our method falters at filtering intensely flickering surfaces, but better preserves the spatial coherence of edges. However, we observe that both de-flickering methods remove much geometric information, especially given slow motion or weak ambient illumination. Our benchmarking shows that neither our method nor EFR significantly affects optical flow estimation accuracy, despite reducing event counts by 50-65%. Overall, we conclude that the niche benefits of spatial filtering are nullified by the result that filtering hardly affects optical flow estimation.

An event-based camera enables capturing a video at a high temporal resolution, high dynamical range, reduced power consumption and minimal data bandwidth while the camera has minimal physical dimensions compared to a frame-based camera with the same vision properties. The limiting factor, however, of an event-based camera is the spatial resolution which ranges between 40 × 40 and 1280 × 960. To counter this deficiency, a method is researched to super resolve event-based vision in order to enhance spatial resolution. A selection of different neural network types and configurations are researched in a step-by-step fashion. Subsequent experiments tested the selected networks on their ability to process event-based data and extract features from it. Followed by experiments that exploited the limitations of the networks to super resolve at different ratios, lengths of eventstreams and more complex event-based data. Results of various experiments showed that a network configuration that utilizes a transformer architecture was best able to super resolve event-based vision. This type of network leverages the ability to extract features based on dependencies between events which aligns with the characteristics of event- based vision. Based on the obtained results from the exper- iments, a pipeline is proposed to super resolve event-based vision and consists of a combination of a transformer network, multilayer perceptrons and a k-nearest-neighbor algorithm. Using this pipeline, eventstreams can be super resolved in the spatial resolution at a scaling ratio of 4. Visually, these super resolved eventstreams resemble more detailed and enhanced version to the low-resolution input. This proposed pipeline can be considered as a starting point in further research toward the super-resolution of event-based data and thereby contributes to the extension of application possibilities of event-based vision.

Computer vision tasks have shown to benefit greatly from both developments in deep learning networks, and the emergence of event cameras. Deep networks can require a large amount of training data, which is not readily available for event cameras, specifically for optical flow estimation. The need for simulating this data in a realistic, physics-driven manner is therefore crucial. This paper compares the state of the art event camera simulators on different criteria, including event timestamp modeling, performance under low illumination, bandwidth simulation, computation speed and various types of noise simulation. We also summarize the shortcomings of some commonly used optical flow event datasets. For generating high-quality, realistic events, The V2E and DVS-Voltmeter simulators have shown to produce the most accurate data.

Event cameras are bio-inspired sensors with high dynamic range, high temporal resolution, and low power consumption. These features enable precise motion detection even in challenging lighting conditions and fast-changing scenes, rendering them well-suited for optical flow estimation. However, event camera output is sparse and unstructured, making it challenging to process. Transformer architectures have shown to be effective in capturing long-term temporal dependencies and processing sparse input, hence they might be better suited to processing this output by leveraging the fine time granularity inherent to event camera data. We introduce E-GMFlow, an approach for event-based optical flow inspired by the recent success in terms of accuracy of transformer-based models for frame-based optical flow. We explore the effect of temporal details on the accuracy of this transformer architecture by changing the number of temporal bins in which events are discretized. We observe that the increase in the number of temporal bins generally causes higher accuracy and comment on the limitations of this study.

Optical flow estimation with event cameras encompasses two primary algorithm classes: model-based and learning-based methods. Model-based approaches, do not require any training data while learning-based approaches utilize datasets of events to train neural networks. To effectively apply these algorithms, it's essential to understand their respective strengths and weaknesses. This study compares model-based and learning-based optical flow estimation methods using event cameras, aiming to provide guidance for real-world applications. We evaluated these methods on the MVSEC and DSEC datasets, focusing on their accuracy and runtime. Our findings indicate that model-based methods excel on the MVSEC dataset, characterized by small motions, while learning-based approaches perform better on the more dynamic DSEC dataset. To investigate potential overfitting of learning-based methods to DSEC, we retrained the IDNet and TMA models on the BlinkFlow dataset. The retrained models demonstrated competitive accuracy, surpassing model-based methods which indicates that learning-based models perform better on datasets like DSEC even when not able to overfit. Finally, our analysis on runtime showed that model-based methods achieve real-time performance on CPUs and learning-based methods require a GPU to run in real-time.