# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 1 Introduction  
Recently, a new bio-inspired vision sensor, the event camera [1], has emerged and broke the limitation of the standard camera working principle and frame rate. Different from frame of traditional cameras, the event camera could respond to pixel-wise brightness changes in the scene asynchronously and independently. Due to the low temporal resolution, high latency and highly redundant data, the frame-based camera performs poorly in complex scenes such as high-speed motion scenes and suffers from missed and incorrect corner detection tasks in complex scenes due to the motion blur and the low dynamic range of its output. With the unique imaging mechanism, event cameras are low latency (in the order of microseconds), have low power consumption and have a very high dynamic range (120 dB) and very high temporal resolution. As such, event cameras can capture the scene changing rapidly without motion blur and effectively filter the background, which has brought about a new approach to machine vision and artificial intelligence. Event cameras have been widely applied to various visual tasks such as optical flow estimation [2], simultaneous localization and mapping (SLAM) [3] and feature detection [4]. Feature detection is a basic and core component in machine vision tasks such as robot vision servoing [5] and autonomous driving [6]. As a special feature, corners show drastic grey changes in many directions, which makes them easily distinguishable from the neighbouring pixels. The shortcoming of traditional cameras is that they often result inmissed and false detections of corners, introducing errors to higher-level vision tasks. Event cameras tend to show better performance in complex senses due to their characteristics. Therefore, how to extract corners from asynchronous event streams quickly and accurately is still an open problem. The main existing event-based corner detection algorithms are template-based methods [7], gradient-based methods [8] and hybrid methods [9]. High-resolution event cameras are helpful to obtain more detailed features in these tasks, but the existing higher-resolution event cameras, such as CeleX-V (1280 \(\times\) 800) [10] and Samsung's DVS Gen4 (1280 \(\times\) 960) [11], can generate more than 10 million events per second, dozens of times more events than DAVIS240C which used in event camera datasets [12]; therefore, realizing real-time processing is still a problem. Therefore, to obtain higher performance in complex scenes, it is necessary to explore a more effective method to remove redundant and noisy events as much as possible to reduce computational cost and save computing resources. To address the aforementioned problem, we propose an improved event-based corner detector and design a novel filter and a new local Surface of Active Events (SAE) representation method. The filter uses a double-threshold to remove redundant and noisy events, which is able to significantly improve throughput while maintaining performance. The proposed local SAE representation is a modification of the original Sigmoid function, which is computationally efficient and better reflects the spatio-temporal distribution of events than existing representations. We evaluate our method on public datasets and achieve better performance. The main contributions of this paper are the following:  
1. A simple and effective event-based double threshold filter that reduces redundant and noisy events from the events stream effectively which has better performance than the previous filter and is suitable for the existing event-based corner detector;
2. An efficient local SAE representation method called _Sigmoid_\({}^{*}\)-SAE which has lower computation and clearer spatio-temporal distribution;
3. An asynchronous corner tracker which verifies the effectiveness of our detector. The remainder of the paper is organized as follows. In Section 2, we present the related work about event-based feature extraction. Section 3 describes our proposed detector in detail.