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Riaz's Projects

2018aicity_teamuw icon 2018aicity_teamuw

Source code from the winning team in Track 1 and Track 3 at the AI City Challenge Workshop in CVPR 2018.

acurustrack icon acurustrack

A multi-object tracking component. Works in the conditions where identification and classical object trackers don't (e.g. shaky/unstable camera footage, occlusions, motion blur, covered faces, etc.). Works on any object despite their nature.

amazon-sagemaker-examples icon amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

android-yolo icon android-yolo

Real-time object detection on Android using the YOLO network with TensorFlow

anynet icon anynet

(ICRA) Anytime Stereo Image Depth Estimation on Mobile Devices

apdrawinggan icon apdrawinggan

Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)

artificial-intelligence-deep-learning-machine-learning-tutorials icon artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

ask-fake-ai-karen icon ask-fake-ai-karen

AI-generated talking head video of fake people responding to your input question text.

attendence-system-with-face-detection icon attendence-system-with-face-detection

It is an attendance system which uses Machine Learning concepts for facial detection. Marking attendance only in a fixed time slot, it also sends an E-mail to the student and parents if he/she is absent.

autonomous-forest-surveillance-safety-system-using-opencv icon autonomous-forest-surveillance-safety-system-using-opencv

The current forest surveillance systems methods consume a lot of resources and are less efficient, not reliable and require a constant human presence whose tasks can be easily automated using new technology. To solve these problems we propose an autonomous surveillance system which uses object detection to identify specified animals. It is capable of monitoring forest fires, intruders, wildlife etc, all at once and alerts the concerned officials immediately and precisely. It has a hybrid object detection system using HAAR and Backpropagation neural network algorithms which can be used to train and detect animals and predict from the data obtained respectively. This helps in detecting various unwanted visitors, dangerous animals, or restricted tools into the forest. The system can not only store the video feed but can also determine population , track a specific animal or human and sends the pictures to your email directly along with real-time video monitoring via the internet which allows the users to monitor from anywhere in the world and sends instant alerts to your phone via an SMS even in remote areas in case of emergencies, and it stores all the data in a repository. We can control the system using a windows app which allows us to select which animals to be detected by the camera modules and their alert levels along with other settings and also provides a detailed analysis on various things like forest fires, animal population, trespassed areas etc, to users in simple charts. It is a smart, automatic, modular system which is cheap and easily expandable.

avod icon avod

Code for 3D object detection for autonomous driving

awesome-face_recognition icon awesome-face_recognition

papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;

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