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The split of train. This code has been tested on Ubuntu CUDA 8. Please build matcaffe before running the detection demo. This is determined by K-means. Tip: If the training does not converge, try some other random seeds. You should obtain a fair performance after a few tries. Due to the randomness, you are difficult to fully reproduce the same models, but the performance should be close. We can get the quantitive results average precision in three levels: "easy", "moderate" and "hard" same as the KITTI benchmark.

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Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit acd8d38 Sep 16, Requirements This code has been tested on Ubuntu You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Mar 7, Add files via upload. Aug 20, Mar 12, Sep 16, The detection of vehicles in aerial images is important for various applications e. Collecting traffic and parking data from an airborne platform gives fast coverage over a larger area.

vehicle detection dataset

Getting the same coverage by terrestrial sensors would need the deployment of more sensors, more manual work, thus higher costs. In this real-time system aerial images are captured over roads and the vehicles are detected and tracked across multiple consecutive frames.

vehicle detection dataset

This gives a fast and comprehensive information of the traffic situation by providing the number of vehicles and their position and speed. The vehicle detection is a challenging problem due to the small size of the vehicles a car might be only 30x12 pixels and the complex background of man-made objects which appear visually similar to the cars.

Providing both the position and the orientation of the detected objects supports the tracking by giving constraints on the motion of the vehicles. This is particularly important in dense traffic scenes where the object assignment is more challenging. The utilization of roads and parking lots depends also on the type of the vehicle e.

Vehicle Detection and Pose Estimation for Autonomous Driving - Master's thesis, May 2017

A system having access to this richer information can manage the infrastructure better. In a real-time system, as in Vabene, the processing time and computing power is limited. Therefore the processing method should be as fast as possible. To address these challenges we apply computer vision and machine learning techniques and test our methods both in experimental and operational settings. To help the research we provide a dataset of aerial images with vehicle annotations.

This can be found in the downloads.

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Figure 1: The detected cars are highlighted in an aerial image from our dataset. The green and cyan rectangles show different vehicle types, while the black rectangles show not detected cars. Figure 2: The detected cars are highlighted in an aerial image from our dataset. DLR Portal. Advanced Search. Earth Observation Center. Department: Atmospheric Processors. Department: EO Data Science. Department: Experimental Methods.

Department: Photogrammetry and Image Analysis. Team: Projekte und Missionen. Team: 3D Modeling. Applications and Projects. Satellite Data. Media Library. Related Articles. Traffic Monitoring. All rights reserved. Print Vehicle Detection in Aerial Images Extracting traffic information from the air The detection of vehicles in aerial images is important for various applications e.

Challenges The vehicle detection is a challenging problem due to the small size of the vehicles a car might be only 30x12 pixels and the complex background of man-made objects which appear visually similar to the cars. Dataset over Munich To help the research we provide a dataset of aerial images with vehicle annotations.We focused on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box.

The re-identification problem is solved by using color histograms and histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The applications of this work include finding important parameters such as travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.

Existing methods addressing this problem are difficult to compare due to a lack of a common data set with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what factors are affecting their performance. We captured a new data set of 18 full-HD videos, each around 1 hr long, captured at six different locations.

Vehicles in the videos instances in total are annotated with the precise speed measurements from optical gates using LiDAR and verified with several reference GPS tracks. We made the data set available for download and it contains the videos and metadata calibration, lengths of features in image, annotations, and so on for future comparison and evaluation.

Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this data set in detail. Cheap Rendering vs. We are showing an approach to automatic synthesis of custom datasets, simulating various major influences: viewpoint, camera parameters, sunlight, surrounding environment, etc.

A suitable scene graph accompanied by a set of scripts was created, that allows simple configuration of the synthesized dataset. The generator is also capable of storing rich set of metadata that are used as annotations of the synthesized images. We synthesized several experimental datasets, evaluated their statistical properties, as compared to real-life datasets.

Most importantly, we trained a detector on the synthetic data. Its detection performance is comparable to a detector trained on state-of-the-art real-life dataset.

Synthesis of a dataset of 10, images takes only several hours, which is much more efficient, compared to manual annotation, let aside the possibility of human error in annotation.Autonomous vehicles are a high-interest area of computer vision with numerous applications and a large potential for profit.

As with all computer vision algorithms, autonomous vehicles require a cornucopia of image data to train. It is often difficult to gain access to large amounts of high-quality images or find a reputable image annotation service.

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Below is a list of 10 open image and video datasets great for use in autonomous vehicle research and development. The datasets below consist of overimages and still video frames, some of which are already annotated. The images are divided into the following six categories by vehicle type: bus, microbus, minivan, sedan, SUV, and truck.

Cityscapes Image Pairs — Using traffic videos shot from vehicles driven in Germany, this dataset includes 2, image pairs.

Vehicle Detection in Aerial Images

Each individual image file has the original still frame on the left and the same frame semantically segmented on the right. GTI Vehicle Image Database — This dataset includes 3, rear-angle images of vehicles on the road, as well as 3, images of roads absent of any vehicles. This image dataset includes over 14, images made up of 7, testing images and 7, training images with bounding boxes labels in a separate file.

LISA Traffic Light Dataset — While this dataset does not focus on vehicles, it is still a very useful image dataset for training autonomous vehicle algorithms. The LISA Traffic Light Dataset includes both nighttime and daytime videos totaling 43, frames which includeannotated traffic lights. The focus of this dataset is traffic lights.

vehicle detection dataset

However, almost all the frames have both traffic lights and vehicles within them. Nepalese Vehicles — Consisting of a total of 30 traffic videos taken in the streets of Kathmandu, this dataset includes images of 4, vehicles cropped from those videos. Of the 4, images, 1, are of two-wheeled vehicles and 2, are of four-wheeled vehicles. Rain and Snow Traffic Surveillance — This dataset consists of 22 videos each around five minutes.

The videos were captured using both an RGB color camera and an infrared thermal camera. Therefore, the data includes overRGB-thermal image pairs. Semantic Segmentation for Self Driving Cars — Created as part of the Lyft Udacity Challenge, this dataset includes 5, images and corresponding semantic segmentation labels.Elon Musk at TED. Source code and a more technically elaborated writeup are available on GitHub.

To write a software pipeline to identify vehicles in a video from a front-facing camera on a car. In my implementation, I used a Deep Learning approach to image recognition. It turns out CNNs are suitable for these type of problems as well. Essentially, this would be somewhat equal to:.

Udacity equips students with the great resources for training the classifier. The Final model had difficulties in detecting the white Lexus in the Project video, so I augmented the dataset with about samples of it.

Additionally, I used the same random image augmentation technique as in Project 2 for Traffic Signs Classificationyielding about images of vehicles from the Project video.

Obviously each sample had been horizontally flipped, additionally inflating the dataset by a factor of 2. As a result, I had approximately data points. An equal number of non-vehicle images has been added as negative examples.

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I borrowed the technique of constructing the top of the network from the implementation of Max Ritterwho apparently employed the same approach. A lot of model architectures with varying complexity have been tested to derive a final model. I added my top single-feature binary classifier and fine-tuned the model. The flip side with the VGG is that it is rather complex, making predictions computationally heavy. I then tested some custom CNN configurations of varying number of layers and shapes, incrementally reducing complexity and evaluating test accuracy, and finally arrived at the model with as little as about 28, trainable parameters with test accuracy of still about The model has been implemented and trained using Keras with TensorFlow backend.

Sample predictions results:. Original frame from the video stream looks like this:. It is not strictly original, as it has already been subjected to undistortion, but that deserves a story of its own. For the task at hand, this is the image to be processed by the vehicle detection pipeline. The region of interest for the vehicle detection starts at an approximately th pixel from the top and spans vertically for about pixels. Thus, we have a region of interest with the dimensions of xstarting at th pixel vertically.

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This transforms the dimensionality of the top convolutional layer from? The vehicle scanning pipeline consists the following steps:. Produce the detection map using trained CNN model:. Apply the confidence threshold to generate the binary map:. The predictions are very polarized, that is, they mostly stick to Ones and Zeros for vehicles and non-vehicle points.

vehicle detection dataset

Therefore, even the midpoint of 0. Just to be on safe side, I stick with 0. Label the obtained detection areas with the label function of the scipy. This is also the first approximation of detected vehicles.

Just to illustrate the result of this points-to-squares transformation projected onto the original image:. Create the Heat Map. Save labeled features of the Heat Map to the list of labels, where they would be kept for a certain number of consequent frames. The final step is getting the actual bounding boxes for the vehicles.

OpenCV provides the handy function cv2.Vehicle Detection and Tracking. The complete code can be found here. The basic objective of this project is to apply the concepts of HOG and Machine Learning to detect a Vehicle from a dashboard video.

Wait a minute? Machine Learning and that too for Object detection in ? Sure, the Deep Learning implementations like YOLO and SSD that utilize convolutional neural network stand out for this purpose but when you are a beginner in this field, its better to start with the classical approach. The most important thing for any machine learning problem is the labelled data set and here we need to have two sets of data: Vehicle and Non Vehicle Images.

The images are of size 64x64 and somewhat looked like this Once we have got the dataset the next obvious step is to extract the features from the images. But why? My friend, if we do so It will take ages to process the image and just a reminder we are not feeding images to CNN here and this is not a Deep Learning Problem after all!

Ok got it, but still how to extract the features? Well, there are three good methods if you want to extract features from the images. Well certainly you are correct on this point. We can extract all the information from the image by flattening it using numpy. Close to 12k features from a single image is not a good idea! So here Spatial Binning comes to picture! What if I say, a 64x64 image gives the same information as 16x16 gives?

Of course there is some loss of information but still we are able to extract good features out of the image! So if I apply numpy. HOG actually takes an image, divides it into various blocks in which we have cells, in cells we observe the pixels and extract the feature vectors from them. The pixels inside the cell are classified into different orientations and the resulting vector for a particular cell inside a block is decided by the magnitude of the strongest vector.

Note- here we are not counting the occurrence of a pixel in a particular orientation but instead we are interested in the magnitude of the pixel in that particular orientation.

Vehicle Detection and Tracking using Machine Learning and HOG

To read more about HOG this is a good link. Just a point to note here. This is because HOG internally performs some computations and reduces the redundancies in the data and returns optimized feature vectors.

Also more the number of lines you see in the image means it will return more features. Ok, cool Now we know how to extract features so we will process these steps for all images? Yes, you are right but it is not necessary to use all features from all the methods above. After a lot of hit and trials I decided to go with the following Cool, after running images through the HOG function with these parameters the final parameter size comes out to be which is pretty cool!

Data Preprocessing Now our features are ready the next step is to pre-process the data. We can perform following preprocessing An very important point here to note is that after Step ii we have to fit and transform the data, but we should not fit the data in the test set because we do not want our classifier to sneak peak into our data.

Well, features are extracted, the data is preprocessed! What next? Yup, now comes the turn of our classifier. The choice of classifier is yours but there are a plenty to chose fromDocumentation Help Center. This example shows how to train a vision-based vehicle detector using deep learning. Vehicle detection using computer vision is an important component for tracking vehicles around the ego vehicle.

The ability to detect and track vehicles is required for many autonomous driving applications, such as for forward collision warning, adaptive cruise control, and automated lane keeping. However, the pretrained models might not suit every application, requiring you to train from scratch. This example shows how to train a vehicle detector from scratch using deep learning. Deep learning is a powerful machine learning technique that you can use to train robust object detectors.

Download a pretrained detector to avoid having to wait for training to complete. If you want to train the detector, set the doTrainingAndEval variable to true. This example uses a small labeled dataset that contains images. Each image contains one or two labeled instances of a vehicle. A small dataset is useful for exploring the Faster R-CNN training procedure, but in practice, more labeled images are needed to train a robust detector.

Unzip the vehicle images and load the vehicle ground truth data. The vehicle data is stored in a two-column table, where the first column contains the image file paths and the second column contains the vehicle bounding boxes. Split the data set into a training set for training the detector and a test set for evaluating the detector.

Detection of Vehicles and Datasets

Use the rest for evaluation. Use imageDatastore and boxLabelDatastore to create datastores for loading the image and label data during training and evaluation. A Faster R-CNN object detection network is composed of a feature extraction network followed by two subnetworks. The first subnetwork following the feature extraction network is a region proposal network RPN trained to generate object proposals - areas in the image where objects are likely to exist.

The second subnetwork is trained to predict the actual class of each object proposal. This example uses ResNet for feature extraction. You can also use other pretrained networks such as MobileNet v2 or ResNet, depending on your application requirements. First, specify the network input size. When choosing the network input size, consider the minimum size required to run the network itself, the size of the training images, and the computational cost incurred by processing data at the selected size.

When feasible, choose a network input size that is close to the size of the training image and larger than the input size required for the network. To reduce the computational cost of running the example, specify a network input size of [ 3], which is the minimum size required to run the network. Note that the training images used in this example are bigger than by and vary in size, so you must resize the images in a preprocessing step prior to training.

Next, use estimateAnchorBoxes to estimate anchor boxes based on the size of objects in the training data. To account for the resizing of the images prior to training, resize the training data for estimating anchor boxes. Use transform to preprocess the training data, then define the number of anchor boxes and estimate the anchor boxes.

This feature extraction layer outputs feature maps that are downsampled by a factor of This amount of downsampling is a good trade-off between spatial resolution and the strength of the extracted features, as features extracted further down the network encode stronger image features at the cost of spatial resolution. Choosing the optimal feature extraction layer requires empirical analysis. You can use analyzeNetwork to find the names of other potential feature extraction layers within a network.

Data augmentation is used to improve network accuracy by randomly transforming the original data during training. By using data augmentation, you can add more variety to the training data without actually having to increase the number of labeled training samples.

Use transform to augment the training data by randomly flipping the image and associated box labels horizontally. Note that data augmentation is not applied to test data. Ideally, test data is representative of the original data and is left unmodified for unbiased evaluation. Use trainingOptions to specify network training options.