Address Event Representation is a thriving technology that could change digital image processing paradigm. This paper proposes a methodology to characterize the shape of objects using the streaming of asynchronous events. A new descriptor that enhances spikes connectivity is associated with two oriented histogram based representations. This paper uses these features to develop both a non-supervised and a supervised multi-classification framework to recognize poker symbols from the Poker-DVS public dataset. The aforementioned framework, which uses a very limited number of events and a simple class modeling, yields results that challenge more sophisticated methodologies proposed by the state of the art. A feature family based on context shapes is applied to the more challenging 2015 Poker-DVS dataset with a supervised classifier obtaining an accuracy of 98.5 . The system is also applied to the MNIST-DVS dataset yielding an accuracy of 94.6 and 96.3 on digit recognition, for scales 4 and 8 respectively.
In @cite_39 , the low latency of the DVS was utilized to capture highway traffic flow. Moving vehicles were detected by accumulating the spikes lying on a region of interest within a window of 10 ms and stored in a buffer. The presence of a vehicle was validated by an established threshold in the number of activated pixels.
@cite_39
2074091263 2068356831 2033614958 2061055677
This paper focuses on real-time pedestrian detection on Field Programmable Gate Arrays (FPGAs) using the Histograms of Oriented Gradients (HOG) descriptor in combination with a Support Vector Machine (SVM) for classification as a basic method. We propose to process image data at twice the pixel frequency and to normalize blocks with the L1-Sqrt-norm resulting in an efficient resource utilization. This implementation allows for parallel computation of different scales. Combined with a time-multiplex approach we increase multiscale capabilities beyond resource limitations. We are able to process 64 high resolution images (1920 × 1080 pixels) per second at 18 scales with a latency of less than 150 u s. 1.79 million HOG descriptors and their SVM classifications can be calculated per second and per scale, which outperforms current FPGA implementations by a factor of 4. This article presents an embedded multilane traffic data acquisition system based on an asynchronous temporal contrast vision sensor, and algorithms for vehicle speed estimation developed to make efficient use of the asynchronous high-precision timing information delivered by this sensor. The vision sensor features high temporal resolution with a latency of less than 100 µs, wide dynamic range of 120 dB of illumination, and zero-redundancy, asynchronous data output. For data collection, processing and interfacing, a low-cost digital signal processor is used. The speed of the detected vehicles is calculated from the vision sensor's asynchronous temporal contrast event data. We present three different algorithms for velocity estimation and evaluate their accuracy by means of calibrated reference measurements. The error of the speed estimation of all algorithms is near zero mean and has a standard deviation better than 3 for both traffic flow directions. The results and the accuracy limitations as well as the combined use of the algorithms in the system are discussed. At the current state of the art, the agility of an autonomous flying robot is limited by its sensing pipeline, because the relatively high latency and low sampling frequency limit the aggressiveness of the control strategies that can be implemented. To obtain more agile robots, we need faster sensing pipelines. A Dynamic Vision Sensor (DVS) is a very different sensor than a normal CMOS camera: rather than providing discrete frames like a CMOS camera, the sensor output is a sequence of asynchronous timestamped events each describing a change in the perceived brightness at a single pixel. The latency of such sensors can be measured in the microseconds, thus offering the theoretical possibility of creating a sensing pipeline whose latency is negligible compared to the dynamics of the platform. However, to use these sensors we must rethink the way we interpret visual data. This paper presents a method for low-latency pose tracking using a DVS and Active Led Markers (ALMs), which are LEDs blinking at high frequency (>1 KHz). The sensor's time resolution allows distinguishing different frequencies, thus avoiding the need for data association. This approach is compared to traditional pose tracking based on a CMOS camera. The DVS performance is not affected by fast motion, unlike the CMOS camera, which suffers from motion blur. It has great significance to acquire vehicle speed for active safety system. This paper presents a methodology for identifying vehicle speed by obtaining a sparse optical flow from image sequences. Distinct corners can be detected by Harris corner detector after image enhancement. Then, Lucas-Kanade method for optical flow calculation is utilized to match the sparse feature set of one frame on the consecutive frame. In order to improve the accuracy of optical flow, RANSAC algorithm is introduced to optimize the matched corners. Finally, the vehicle speed can be determined by averaging all the speeds estimated by every optimized matched corner. The results of field test indicated that the computation time of the developed method to execute for one time was 59ms, and the mean error of speed estimation relative to the measurement of GPS was 0.121 m s. The developed method can achieve satisfying performance, such as accuracy and output frequency.