Events cameras are ideal sensors for enabling robots to detect and track objects in highly dynamic environments due to their low latency output, high temporal resolution, and high dynamic range. In this paper, we present the Asynchronous Event Multi-Object Tracking (AEMOT) algorithm for detecting and tracking multiple objects by processing individual raw events asynchronously. AEMOT detects salient event blob features by identifying regions of consistent optical flow using a novel Field of Active Flow Directions built from the Surface of Active Events. Detected features are tracked as candidate objects using the recently proposed Asynchronous Event Blob (AEB) tracker in order to construct small intensity patches of each candidate object. A novel learnt validation stage promotes or discards candidate objects based on classification of their intensity patches, with promoted objects having their position, velocity, size, and orientation estimated at their event rate. We evaluate AEMOT on a new Bee Swarm Dataset, where it tracks dozens of small bees with precision and recall performance exceeding that of alternative event-based detection and tracking algorithms by over 37%. Source code and the labelled event Bee Swarm Dataset will be open sourced.11https://github.com/angus-apps/AEMOT

Event cameras are ideally suited to capture High Dynamic Range (HDR) visual information without blur but provide poor imaging capability for static or slowly varying scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on HDR or quickly changing scenes. In this paper, we present an asynchronous linear filter architecture, fusing event and frame camera data, for HDR video reconstruction and spatial convolution that exploits the advantages of both sensor modalities. The key idea is the introduction of a state that directly encodes the integrated or convolved image information and that is updated asynchronously as each event or each frame arrives from the camera. The state can be read-off as-often-as and whenever required to feed into subsequent vision modules for real-time robotic systems. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions, along with a new dataset with HDR reference that we provide. The proposed AKF pipeline outperforms other state-of-the-art methods in both absolute intensity error (69.4% reduction) and image similarity indexes (average 35.5% improvement). We also demonstrate the integration of image convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an application of our architecture.

Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this article, we propose a novel algorithm for tracking event blobs using raw eventsasynchronouslyin real time. We introduce the concept of anevent blobas a spatio-temporal likelihood of event occurrence where the conditional spatial likelihood is blob-like. Many real-world objects, such as car headlights or any quickly moving foreground objects, generate event blob data. The proposed algorithm uses a nearest neighbor classifier with a dynamic threshold criteria for data association coupled with an extended Kalman filter to track the event blob state. Our algorithm achieves highly accurate blob tracking, velocity estimation, and shape estimation even under challenging lighting conditions and high-speed motions ($>$11 000 pixels/s). The microsecond time resolution achieved means that the filter output can be used to derive secondary information, such as time-to-contact or range estimation, that will enable applications to real-world problems, such as collision avoidance, in autonomous driving.

Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor. Such cameras are ideal for robotics applications since they have high temporal resolution, high dynamic range and low latency. However, due to their high temporal resolution, event cameras are particularly sensitive to flicker such as from fluorescent or LED lights. During every cycle from bright to dark, pixels that image a flickering light source generate many events that provide little or no useful information for a robot, swamping the useful data in the scene. In this paper, we propose a novel linear filter to preprocess event data to remove unwanted flicker events from an event stream. The proposed algorithm achieves over 4.6 times relative improvement in the signal-to-noise ratio when compared to the raw event stream due to the effective removal of flicker from fluorescent lighting. Thus, it is ideally suited to robotics applications that operate in indoor settings or scenes illuminated by flickering light sources. Code, Datasets and Video: https://github.com/ziweiWWANG/EFR

Event cameras are bio-inspired dynamic vision sensors that respond to changes in image intensity with a high temporal resolution, high dynamic range and low latency. These sensor characteristics are ideally suited to enable visual target tracking in concert with a broadcast visual communication channel for smart visual beacons with applications in distributed robotics. Visual beacons can be constructed by high-frequency modulation of Light Emitting Diodes (LEDs) such as vehicle headlights, Internet of Things (IoT) LEDs, smart building lights, etc., that are already present in many real-world scenarios. The high temporal resolution characteristic of the event cameras allows them to capture visual signals at far higher data rates compared to classical frame-based cameras. In this paper, we propose a novel smart visual beacon architecture with both LED modulation and event camera demodulation algorithms. We quantitatively evaluate the relationship between LED transmission rate, communication distance and the message transmission accuracy for the smart visual beacon communication system that we prototyped. The proposed method achieves up to 4 kbps in an indoor environment and lossless transmission over a distance of 100 meters, at a transmission rate of 500 bps, in full sunlight, demonstrating the potential of the technology in an outdoor environment.

Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement).

Stereo camera systems play an important role in robotics applications to perceive the 3D world. However, conventional cameras have drawbacks such as low dynamic range, motion blur and latency due to the underlying frame- based mechanism. Event cameras address these limitations as they report the brightness changes of each pixel independently with a fine temporal resolution, but they are unable to acquire absolute intensity information directly. Although integrated hybrid event-frame sensors (e.g., DAVIS) are available, the quality of data is compromised by coupling at the pixel level in the circuit fabrication of such cameras. This paper proposes a stereo hybrid event-frame (SHEF) camera system that offers a sensor modality with separate high-quality pure event and pure frame cameras, overcoming the limitations of each separate sensor and allowing for stereo depth estimation. We provide a SHEF dataset targeted at evaluating disparity estimation algorithms and introduce a stereo disparity estimation algorithm that uses edge information extracted from the event stream correlated with the edge detected in the frame data. Our disparity estimation outperforms the state-of-the-art stereo matching algorithm on the SHEF dataset.