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It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. This dataset works well for several reasons. # Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the () dataset. I recommend you walk through those if interested in training a custom object detector from scratch. While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail() and () ].
Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). > If you are not interested in the process of training the detector, you can skip straight to applying the (#detecting-hands). More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see () ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. But things are changing with advances in fast neural networks. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset.
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For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see () paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust.
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> P.S if you are using or have used the models provided here, feel free to reach out on twitter and share your work! # Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. You may need to () graph using the (model-checkpoint) in the repo to fit your TF version. Using a different version may result in ().
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Some fps numbers are: | FPS | Image Size | Device| Comments| | - | - | - | - | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Realtime detection on video stream from a webcam. If you use this tutorial or models in your research or project, please cite (#citing-this-tutorial). Better still, provide code that can be adapted to other uses cases. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). I then tried the () which was a much better fit to my requirements. I experimented first with the () (the results were not good). I was interested mainly in detecting hands on a table (egocentric view point). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). GitHub - molyswu/hand_detection: using Neural Networks (SSD) on Tensorflow.