# Video object detection using Visual AI
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/obj-detection.html

> Use Visual AI for object detection in video streams.

# Video object detection using Visual AI

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/object_detection_on_video)

Object detection (binary and multiclass classification) applied to image and video processing is one of the tasks that can be easily and efficiently implemented with DataRobot [Visual AI](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/index.html), which allows you to train deep learning models intended for Computer Vision based-projects. You can also bring your own Computer Vision model and deploy it in DataRobot via the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html).

This accelerator demonstrates how deep learning models trained and deployed with DataRobot can be used for object detection on a video stream. (Consider the example of detection when the person in front of the camera wears glasses.) The Elastic-Net Classifier (L2 / Binomial Deviance) along with Pretrained MobileNetV3-Small-Pruned Multi-Level Global Average Pooling Image Featurizer with no image augmentation are used in this accelerator. The dataset used contains images for two classes: persons with glasses and persons without glasses and is linked in the accelerator on GitHub. A sample of the dataset (100 images for each class) is used for this accelerator. The video stream is captured with OpenCV Computer Vision library. The frontend is implemented as a Streamlit application.

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