In this technical report we investigate speed estimation of the ego-vehicle on the KITTI benchmark using state-of-the-art deep neural network based optical flow and single-view depth prediction methods. Using a straightforward intuitive approach and approximating a single scale factor, we evaluate several application schemes of the deep networks and formulate meaningful conclusions such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network methods influences speed estimation performance; using the depth and optical flow results from smaller crops of wide images degrades performance. With these observations in mind, we achieve a RMSE of less than 1 m s for vehicle speed estimation using monocular images as input from recordings of the KITTI benchmark. Limitations and possible future directions are discussed as well.
We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.  We aim to determine the speed of ego-vehicle motion from a video stream. Previous work by [1] has shown that motion can be detected and quantified with the help of a synchrony autoencoder, which has multiplicative gating interactions introduced between its hidden units, and hence, across video frames. In this work we modify their synchrony autoencoder method to achieve a ”real time” performance in a wide variety of driving environments. Our modifications led to a model which is 1.5 times faster and uses only half of the total memory by comparison with the original. We also benchmark the speed estimation performance against a model based on CaffeNet. CaffeNet is known for visual classification and localization but we employ its architecture with a little tweak for speed determination using sequential video frames and blur patterns. We evaluate our models on self-collected data, KITTI, and other standard sets. 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.