In the following series I will explore different tools and techniques for doing object detection in streaming video in real time or faster. Starting with the baseline Python detector running slowly and gradually picking up speed. In these series In the course of these posts we will explore optimizing object detection in videos. We will … Continue reading Supercharging Object Detection in Video: from Glacial to Lightning Speed →

We started from the Python object detector performance as baseline (~ 19 fps). Next we ditch Python and all our pre-installed libraries and custom build everything. C++ will become the development environment not just because it’s more “bare bones” than Python and thus more performant but also to access functionality not available in Python. Environment … Continue reading Supercharging Object Detection in Videos: Setup →

Tensorflow C++ Video Detector It is time to validate all this arduous setup work, run our first C++ detector and reap the first benefits. You may clone this repository, which is a fork of this repository, modified and adapted to the modern times. Ensuring the Right Build Paths Note the following excerpt from CMakeLists.txt: The … Continue reading Supercharging Object Detection in Video: First App →

Source code for the finished project is here. NVIDIA TensorRT is a framework used to optimize deep networks for inference by performing surgery on graphs trained with popular deep learning frameworks: Tensorflow, Caffe, etc. Preparing the Tensorflow Graph Our code is based on the Uff SSD sample installed with TensorRT 5.0. The guide together with … Continue reading Supercharging Object Detection in Video: TensorRT 5 →

In the previous post we validated our install and ran a simple detector in C++. It is now time to start optimizing it. Source code for the finished project is here. Optimizing Video Decoding If we build and run the video_reader.cpp OpenCV sample, we will observe a staggering performance improvement available in OpenCV for decoding … Continue reading Supercharging Object Detection in Video: Optimizing Decoding and Graph Feeding →