Event-based Object Research Articles

Low-latency and high-accuracy hybrid image- and event-based object detector

Low-latency and high-accuracy hybrid image- and event-based object detector

Continuous-time Object Segmentation using High Temporal Resolution Event Camera.

Event cameras are novel bio-inspired sensors, where individual pixels operate independently and asynchronously, generating intensity changes as events. Leveraging the microsecond resolution (no motion blur) and high dynamic range (compatible with extreme light conditions) of events, there is considerable promise in directly segmenting objects from sparse and asynchronous event streams in various applications. However, different from the rich cues in video object segmentation, it is challenging to segment complete objects from the sparse event stream. In this paper, we present the first framework for continuous-time object segmentation from event stream. Given the object mask at the initial time, our task aims to segment the complete object at any subsequent time in event streams. Specifically, our framework consists of a Recurrent Temporal Embedding Extraction (RTEE) module based on a novel ResLSTM, a Cross-time Spatiotemporal Feature Modeling (CSFM) module which is a transformer architecture with long-term and short-term matching modules, and a segmentation head. The historical events and masks (reference sets) are recurrently fed into our framework along with current-time events. The temporal embedding is updated as new events are input, enabling our framework to continuously process the event stream. To train and test our model, we construct both real-world and simulated event-based object segmentation datasets, each comprising event streams, APS images, and object annotations. Extensive experiments on our datasets demonstrate the effectiveness of the proposed recurrent architecture. Our code and dataset are available at https://sites.google.com/view/ecos-net/.

SpikingViT: a Multi-scale Spiking Vision Transformer Model for Event-based Object Detection

SpikingViT: a Multi-scale Spiking Vision Transformer Model for Event-based Object Detection

From Dense to Sparse: Low-Latency and Speed-Robust Event-Based Object Detection

From Dense to Sparse: Low-Latency and Speed-Robust Event-Based Object Detection

Improving Multispike Learning With Plastic Synaptic Delays.

Emulating the spike-based processing in the brain, spiking neural networks (SNNs) are developed and act as a promising candidate for the new generation of artificial neural networks that aim to produce efficient cognitions as the brain. Due to the complex dynamics and nonlinearity of SNNs, designing efficient learning algorithms has remained a major difficulty, which attracts great research attention. Most existing ones focus on the adjustment of synaptic weights. However, other components, such as synaptic delays, are found to be adaptive and important in modulating neural behavior. How could plasticity on different components cooperate to improve the learning of SNNs remains as an interesting question. Advancing our previous multispike learning, we propose a new joint weight-delay plasticity rule, named TDP-DL, in this article. Plastic delays are integrated into the learning framework, and as a result, the performance of multispike learning is significantly improved. Simulation results highlight the effectiveness and efficiency of our TDP-DL rule compared to baseline ones. Moreover, we reveal the underlying principle of how synaptic weights and delays cooperate with each other through a synthetic task of interval selectivity and show that plastic delays can enhance the selectivity and flexibility of neurons by shifting information across time. Due to this capability, useful information distributed away in the time domain can be effectively integrated for a better accuracy performance, as highlighted in our generalization tasks of the image, speech, and event-based object recognitions. Our work is thus valuable and significant to improve the performance of spike-based neuromorphic computing.

E-detector: Asynchronous Spatio-temporal for Event-based Object Detection in Intelligent Transportation System

In intelligent transportation systems, various sensors, including radar and conventional frame cameras, are used to improve system robustness in various challenging scenarios. An event camera is a novel bio-inspired sensor that has attracted the interest of several researchers. It provides a form of neuromorphic vision to capture motion information asynchronously at high speeds. Thus, it possesses advantages for intelligent transportation systems that conventional frame cameras cannot match, such as high temporal resolution, high dynamic range, as well as sparse and minimal motion blur. Therefore, this study proposes an E-detector based on event cameras that asynchronously detect moving objects. The main innovation of our framework is that the spatiotemporal domain of the event camera can be adjusted according to different velocities and scenarios. It overcomes the inherent challenges that traditional cameras face when detecting moving objects in complex environments, such as high speed, complex lighting, and motion blur. Moreover, our approach adopts filter models and transfer learning to improve the performance of event-based object detection. Experiments have shown that our method can detect high-speed moving objects better than conventional cameras using state-of-the-art detection algorithms. Thus, our proposed approach is extremely competitive and extensible, as it can be extended to other scenarios concerning high-speed moving objects. The study findings are expected to unlock the potential of event cameras in intelligent transportation system applications.

Better and Faster: Adaptive Event Conversion for Event-Based Object Detection

Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect sparse event streams with many advantages. In this paper, we focus on building better and faster event-based object detectors. To this end, we first propose a computationally efficient event representation Hyper Histogram, which adequately preserves both the polarity and temporal information of events. Then we devise an Adaptive Event Conversion module, which converts events into Hyper Histograms according to event density via an adaptive queue. Moreover, we introduce a novel event-based augmentation method Shadow Mosaic, which significantly improves the event sample diversity and enhances the generalization ability of detection models. We equip our proposed modules on three representative object detection models: YOLOv5, Deformable-DETR, and RetinaNet. Experimental results on three event-based detection datasets (1Mpx, Gen1, and MVSEC-NIGHTL21) demonstrate that our proposed approach outperforms other state-of-the-art methods by a large margin, while achieving a much faster running speed (< 14 ms and < 4 ms for 50 ms event data on the 1Mpx and Gen1 datasets).

Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation

Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Compared with frame-based sensors, event cameras have microsecond-level latency and high dynamic range, hence showing great potential for object detection under high-speed motion and poor illumination conditions. Due to sparsity and asynchronism nature with event streams, most of existing approaches resort to hand-crafted methods to convert event data into 2D grid representation. However, they are sub-optimal in aggregating information from event stream for object detection. In this work, we propose to learn an event representation optimized for event-based object detection. Specifically, event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars at neighboring time intervals. Extensive experiments on the recently released event-based automotive detection dataset demonstrate the effectiveness of the proposed method.

SCTN: Event-based object tracking with energy-efficient deep convolutional spiking neural networks.

Event cameras are asynchronous and neuromorphically inspired visual sensors, which have shown great potential in object tracking because they can easily detect moving objects. Since event cameras output discrete events, they are inherently suitable to coordinate with Spiking Neural Network (SNN), which has a unique event-driven computation characteristic and energy-efficient computing. In this paper, we tackle the problem of event-based object tracking by a novel architecture with a discriminatively trained SNN, called the Spiking Convolutional Tracking Network (SCTN). Taking a segment of events as input, SCTN not only better exploits implicit associations among events rather than event-wise processing, but also fully utilizes precise temporal information and maintains the sparse representation in segments instead of frames. To make SCTN more suitable for object tracking, we propose a new loss function that introduces an exponential Intersection over Union (IoU) in the voltage domain. To the best of our knowledge, this is the first tracking network directly trained with SNN. Besides, we present a new event-based tracking dataset, dubbed DVSOT21. In contrast to other competing trackers, experimental results on DVSOT21 demonstrate that our method achieves competitive performance with very low energy consumption compared to ANN based trackers with very low energy consumption compared to ANN based trackers. With lower energy consumption, tracking on neuromorphic hardware will reveal its advantage.

High-temporal-resolution event-based vehicle detection and tracking

Event-based vision has been rapidly growing in recent years justified by the unique characteristics it presents such as its high temporal resolutions (~1us), high dynamic range (>120dB), and output latency of only a few microseconds. This work further explores a hybrid, multi-modal, approach for object detection and tracking that leverages state-of-the-art frame-based detectors complemented by hand-crafted event-based methods to improve the overall tracking performance with minimal computational overhead. The methods presented include event-based bounding box (BB) refinement that improves the precision of the resulting BBs, as well as a continuous event-based object detection method, to recover missed detections and generate inter-frame detections that enable a high-temporal-resolution tracking output. The advantages of these methods are quantitatively verified by an ablation study using the higher order tracking accuracy (HOTA) metric. Results show significant performance gains resembled by an improvement in the HOTA from 56.6%, using only frames, to 64.1% and 64.9%, for the event and edge-based mask configurations combined with the two methods proposed, at the baseline framerate of 24Hz. Likewise, incorporating these methods with the same configurations has improved HOTA from 52.5% to 63.1%, and from 51.3% to 60.2% at the high-temporal-resolution tracking rate of 384Hz. Finally, a validation experiment is conducted to analyze the real-world single-object tracking performance using high-speed LiDAR. Empirical evidence shows that our approaches provide significant advantages compared to using frame-based object detectors at the baseline framerate of 24Hz and higher tracking rates of up to 500Hz.

Real-time grasping strategies using event camera

Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. With advantages of microsecond-level sampling rate and no motion blur of event camera, the model-based and model-free approaches are developed for known and unknown objects’ grasping respectively. The event-based multi-view approach is used to localize the objects in the scene in the model-based approach, and then point cloud processing is utilized to cluster and register the objects. The proposed model-free approach, on the other hand, utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. Using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper, the proposed approaches are experimentally validated with objects of different sizes. Furthermore, it demonstrates robustness and a significant advantage over grasping with a traditional frame-based camera in low-light conditions.

MVF-Net: A Multi-View Fusion Network for Event-Based Object Classification

Event-based object recognition has drawn increasing attention for event cameras’ distinguished advantages of low power consumption and high dynamic range. For this new modality, previous works based on customizing low-level descriptors are vulnerable to noise and with limited generalizability. Although recent works turn to design various deep neural networks to extract event features, they either suffer from data insufficiency to fully train the event-based model or fail to encode spatial and temporal cues simultaneously with their single view network. In this work, we address these limitations by proposing a multi-view attention-aware network, in which an event stream is projected to multi-view 2D maps to utilize well-trained 2D models and explore spatio-temporal complements. Besides, the attention mechanism is used to boost the complements in different streams for better joint inference. Comprehensive experiments show the large superiority of our model over state-of-the-art methods as well as the efficacy of our multi-view fusion framework for event data.

Learning From Images: A Distillation Learning Framework for Event Cameras.

Event cameras have recently drawn massive attention in the computer vision community because of their low power consumption and high response speed. These cameras produce sparse and non-uniform spatiotemporal representations of a scene. These characteristics of representations make it difficult for event-based models to extract discriminative cues (such as textures and geometric relationships). Consequently, event-based methods usually perform poorly compared to their conventional image counterparts. Considering that traditional images and event signals share considerable visual information, this paper aims to improve the feature extraction ability of event-based models by using knowledge distilled from the image domain to additionally provide explicit feature-level supervision for the learning of event data. Specifically, we propose a simple yet effective distillation learning framework, including multi-level customized knowledge distillation constraints. Our framework can significantly boost the feature extraction process for event data and is applicable to various downstream tasks. We evaluate our framework on high-level and low-level tasks, i.e., object classification and optical flow prediction. Experimental results show that our framework can effectively improve the performance of event-based models on both tasks by a large margin. Furthermore, we present a 10K dataset (CEP-DVS) for event-based object classification. This dataset consists of samples recorded under random motion trajectories that can better evaluate the motion robustness of the event-based model and is compatible with multi-modality vision tasks.

Event-Based Object Detection and Tracking for Space Situational Awareness

In this work, we present an optical space imaging dataset using a range of event-based neuromorphic vision sensors. The unique method of operation of event-based sensors makes them ideal for space situational awareness (SSA) applications due to the sparseness inherent in space imaging data. These sensors offer significantly lower bandwidth and power requirements making them particularly well suited for use in remote locations and space-based platforms. We present the first publicly-accessible event-based space imaging dataset including recordings using sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them for SSA applications. The dataset contains both day time and night time recordings, including simultaneous co-collections from different event-based sensors. Recorded at a remote site, and containing 572 labeled targets with a wide range of sizes, trajectories, and signal-to-noise ratios, this real-world event-based dataset represents a challenging detection and tracking task that is not readily solved using previously proposed methods. We propose a highly optimized and robust feature-based detection and tracking method, designed specifically for SSA applications, and implemented via a cascade of increasingly selective event filters. These filters rapidly isolate events associated with space objects, maintaining the high temporal resolution of the sensors. The results from this simple yet highly optimized algorithm on the space imaging dataset demonstrate robust high-speed event-based detection and tracking which can readily be implemented on sensor platforms in space as well as terrestrial environments.

Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification

Event cameras, with low power consumption, high temporal resolution, and high dynamic range, have been used increasingly in computer vision. These superior characteristics enable event cameras to perform low-energy and high-response object classification tasks in challenging scenarios. Nevertheless, specific encoding methods for event-based classification are required owing to the unconventional output of event cameras. Existing event-based encoding methods have focused on extracting semantic and motion information in event signals. However, two main problems exist in these methods: (i) the motion information of event signals leads to mispredictions by the classifiers. (ii) effective evaluation methods to validate the motion robustness of event-based classification models have yet to be proposed. In this work, we introduce an adaptive motion-agnostic encoder for event streams to address the first problem. The proposed encoder would allow us to extract indistinguishable semantic information from an object in different motion circumstances. In addition, we propose a novel motion inconsistency evaluation method to assess the motion robustness of the classification models. We apply our method to several benchmark datasets and evaluate it using motion consistency and inconsistency testing methods. Classification performance shows that our proposed encoder outperforms state-of-the-art methods by a large margin.

End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still in its infancy. The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. To achieve this goal, we propose a synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which effectively encodes the spatio-temporal information of asynchronous retinal events into TSLTD frames with clear motion patterns. We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression. Our method is compared with state-of-the-art object tracking methods, that are based on conventional cameras or event cameras. The experimental results show the superiority of our method in handling various challenging environments such as fast motion and low illumination conditions.

Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras.

We present the first purely event-based, energy-efficient approach for dynamic object detection and categorization with a freely moving event camera. Compared to traditional cameras, event-based object recognition systems are considerably behind in terms of accuracy and algorithmic maturity. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional object representation when hardware resources are limited to implement PCA. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance compared to state-of-the-art algorithms. Additionally, we verified the real-time FPGA performance of the proposed object detection method, trained with limited data as opposed to deep learning methods, under a closed-loop aerial vehicle flight mode. We also compare the proposed object categorization framework to pre-trained convolutional neural networks using transfer learning and highlight the drawbacks of using frame-based sensors under dynamic camera motion. Finally, we provide critical insights about the feature extraction method and the classification parameters on the system performance, which aids in understanding the framework to suit various low-power (less than a few watts) application scenarios.

An End-to-End Broad Learning System for Event-Based Object Classification

Event cameras are bio-inspired vision sensors measuring brightness changes (referred to as an `event') for each pixel independently, instead of capturing brightness images at a fixed rate using conventional cameras. Asynchronous event data mixed with noise information is challenging for event-based vision tasks. In this paper, we propose a broad learning network for object detection using the event data. The broad learning network consists of two distinct layers, a feature-node layer and an enhancement-node layer. Different to convolutional neural networks, the broad learning network can be extended by adding nodes into layers during training. We design a gradient descent algorithm to train network parameters, which creates an event-based broad learning network in an end-to-end manner. Our model outperforms state-of-the-art models, specifically, because of the small scale and increased speed displayed by our model during training. This demonstrates the superiority of event cameras towards online training and inference.

Neuromorphic Event-Based 3D Pose Estimation.

Pose estimation is a fundamental step in many artificial vision tasks. It consists of estimating the 3D pose of an object with respect to a camera from the object's 2D projection. Current state of the art implementations operate on images. These implementations are computationally expensive, especially for real-time applications. Scenes with fast dynamics exceeding 30–60 Hz can rarely be processed in real-time using conventional hardware. This paper presents a new method for event-based 3D object pose estimation, making full use of the high temporal resolution (1 μs) of asynchronous visual events output from a single neuromorphic camera. Given an initial estimate of the pose, each incoming event is used to update the pose by combining both 3D and 2D criteria. We show that the asynchronous high temporal resolution of the neuromorphic camera allows us to solve the problem in an incremental manner, achieving real-time performance at an update rate of several hundreds kHz on a conventional laptop. We show that the high temporal resolution of neuromorphic cameras is a key feature for performing accurate pose estimation. Experiments are provided showing the performance of the algorithm on real data, including fast moving objects, occlusions, and cases where the neuromorphic camera and the object are both in motion.