Privacy-oriented Object Detection
Detectiong people and action in low-resolution images
Leveraging state-of-the-art object detection models like YOLOv8 and pioneering preprocessing techniques such as motion stacking, I’ve developed a sophisticated system capable of accurately identifying individuals and their actions in extremely low-resolution settings (64x36 pixels). With a focus on preserving privacy in sensitive areas like homes and optimizing computational efficiency for real-time operation on embedded devices, my project not only advances the field of computer vision but also holds immense practical implications for privacy-preserving surveillance technologies in real-world applications.
The system employs quantization, allowing it to run on a basic PC without a GPU at 2 ms per inference. This makes it capable of real-time performance and simultaneous operation in multiple rooms.
You can also put regular text between your rows of images, even citations (Einstein & Taub, 1950). Say you wanted to write a bit about your project before you posted the rest of the images. You describe how you toiled, sweated, bled for your project, and then… you reveal its glory in the next row of images.
The code is simple. Just wrap your images with <div class="col-sm"> and place them inside <div class="row"> (read more about the Bootstrap Grid system). To make images responsive, add img-fluid class to each; for rounded corners and shadows use rounded and z-depth-1 classes. Here’s the code for the last row of images above:
<div class="row justify-content-sm-center">
<div class="col-sm-8 mt-3 mt-md-0">
{% include figure.liquid path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
</div>
<div class="col-sm-4 mt-3 mt-md-0">
{% include figure.liquid path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
</div>
</div>