This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operation (motion distortion) of present-day 3D spinning lidar sensors. Our detection method uses a motion-compensated freespace querying algorithm and classifies between dynamic (currently moving) and static (currently stationary) labels at the point level. For a quantitative analysis, we establish a benchmark with motion-distorted lidar data using CARLA, an open-source simulator for autonomous driving research. We also provide a qualitative analysis with real data using a Velodyne HDL-64E in driving scenarios. Compared to existing 3D lidar methods that are model-free, our method is unique because of its setting independence and compensation for pointcloud motion distortion.
Class-specific, or model-based, detectors take advantage of prior information of the objects to be detected. @cite_4 model vehicles as 2D bounding boxes, which is applied to 3D data by processing it into a 2D representation. Rather than manually crafting models, recent work focuses on learning methods. @cite_2 (among many others) take lidar and camera data as input to a deep neural network (DNN) and output class-specific detections. While this category is proven to work well, such methods will simply not detect objects for which they have not been trained.
@cite_4 @cite_2
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This paper addresses the problem of vehicle detection using Deep Convolutional Neural Network (ConvNet) and 3D-LIDAR data with application in advanced driver assistance systems and autonomous driving. A vehicle detection system based on the Hypothesis Generation (HG) and Verification (HV) paradigms is proposed. The data inputted to the system is a point cloud obtained from a 3D-LIDAR mounted on board an instrumented vehicle, which is transformed to a Dense-depth Map (DM). The proposed solution starts by removing ground points followed by point cloud segmentation. Then, segmented obstacles (object hypotheses) are projected onto the DM. Bounding boxes are fitted to the segmented objects as vehicle hypotheses (the HG step). Finally, the bounding boxes are used as inputs to a ConvNet to classify verify the hypotheses of belonging to the category ‘vehicle’ (the HV step). In this paper, we present an evaluation of ConvNet using LIDAR-based DMs and also the impact of domain-specific data augmentation on vehicle detection performance. To train and to evaluate the proposed vehicle detection system, the KITTI Benchmark Suite was used. This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. The precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. The 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with state-of-the-art 2D object detectors. To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72 mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. The categories and appearance of salient objects vary from image to image, therefore, saliency detection is an image-specific task. Due to lack of large-scale saliency training data, using deep neural networks (DNNs) with pretraining is difficult to precisely capture the image-specific saliency cues. To solve this issue, we formulate a zero-shot learning problem to promote existing saliency detectors. Concretely, a DNN is trained as an embedding function to map pixels and the attributes of the salient background regions of an image into the same metric space, in which an image-specific classifier is learned to classify the pixels. Since the image-specific task is performed by the classifier, the DNN embedding effectively plays the role of a general feature extractor. Compared with transferring the learning to a new recognition task using limited data, this formulation makes the DNN learn more effectively from small data. Extensive experiments on five data sets show that our method significantly improves accuracy of existing methods and compares favorably against state-of-the-art approaches.