Traditional object recognition approaches apply feature extraction, part deformation handling, occlusion handling and classification sequentially while they are independent from each other. Ouyang and Wang proposed a model for jointly learning of all of the mentioned processes using one deep neural network. We utilized, and manipulated their toolbox in order to apply it in car detection scenarios where it had not been tested. Creating a single deep architecture from these components, improves the interaction between them and can enhance the performance of the whole system. We believe that the approach can be used as a general purpose object detection toolbox. We tested the algorithm on UIUC car dataset, and achieved an outstanding result. The accuracy of our method was 97 while the previously reported results showed an accuracy of up to 91 . We strongly believe that having an experiment on a larger dataset can show the advantage of using deep models over shallow ones.
We propose a model for car detection that benefits from deep learning approaches and is capable of detecting different classes of cars. The approach uses the training data to improve its low-level features. Even though we applied this approach to side view pictures of cars, it is extendable to other views of the cars too. We very closely followed the work by Ouyang @cite_6 and used the same methodology to detect different object class.
@cite_6
2278156142 2792727293 2543332268 225438136
This paper presents a method for learning an And-Or model to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). First, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on CAD simulations. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Second, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets—the KITTI dataset [1] , the PASCAL VOC2007 car dataset [2] , and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation—the PASCAL VOC2006 car dataset [2] , the 3D car dataset [3] , and the PASCAL3D+ car dataset [4] . Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation. In this paper, a Deep Learning system for accurate road detection is proposed using the ResNet-101 network with a fully convolutional architecture and multiple upscaling steps for image interpolation. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixelwise changes, such as affine and perspective transformations, mirroring, image cropping, distortions, blur, noise, and color changes. In addition, this paper shows that the use of a 4-step upscaling strategy provides optimal learning results as compared to other similar techniques that perform data upscaling based on shallow layers with scarce representation of the scene data. The complete system is trained and tested on data from the KITTI benchmark and besides it is also tested on images recorded on the Campus of the University of Alcala (Spain). The improvement attained after performing data augmentation and conducting a number of training variants is really encouraging, showing the path to follow for enhanced learning generalization of road detection systems with a view to real deployment in self-driving cars. This paper present a part-based approach for detecting objects with large variation of appearance. We extract local image patches as local features both from the object and from the background in training images to learn an object part model discriminatively. Our object part model discriminates the local features whether they are an object part or not. Based on the discrimination results, each local feature casts probabilistic votes for the object location and size which are learned from the training images. Our object part model also requires regression performance for predicting the object location and size through the voting procedure. We build such an object part model with an ensemble of randomized trees trained by splitting each tree node so as to reduce the entropy of class label distribution and the variance of object location and size. Experimental results on hand detection with large pose variation show that our approach outperforms conventional generalized Hough transform. We verified the performance on a public dataset of side-view cars. This paper presents a method of learning reconfigurable hierarchical And-Or models to integrate context and occlusion for car detection. The And-Or model represents the regularities of car-to-car context and occlusion patterns at three levels: (i) layouts of spatially-coupled N cars, (ii) single cars with different viewpoint-occlusion configurations, and (iii) a small number of parts. The learning process consists of two stages. We first learn the structure of the And-Or model with three components: (a) mining N-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining the occlusion configurations based on the overlapping statistics between single cars, and (c) learning visible parts based on car 3D CAD simulation or heuristically mining latent car parts. The And-Or model is organized into a directed and acyclic graph which leads to the Dynamic Programming algorithm in inference. In the second stage, we jointly train the model parameters (for appearance, deformation and bias) using Weak-Label Structural SVM. In experiments, we test our model on four car datasets: the KITTI dataset [11], the street parking dataset [19], the PASCAL VOC2007 car dataset [7], and a self-collected parking lot dataset. We compare with state-of-the-art variants of deformable part-based models and other methods. Our model obtains significant improvement consistently on the four datasets.