Optimizied Video Object Detection

The completed application runs any Model Zoo style object detector in Tensorflow mode (default) and an Inception V2 SSD detector converted from Tensorflow graph to UFF format recognized by TensorRT in TensorRT mode (-t).

Setting up the environment

Read these series of posts

Building the app

• Clone the repo.
• Get the frozen graph and the class labels files for Tensorflow from here
• Get the frozen graph for TensorRT. The class labels file should be available in /usr/src/tensorrt/data/ssd directory.
• Build:

mkdir build
cd build
cmake .. # cmake -DCMAKE_BUILD_TYPE=Debug

Running

Command line options are described in main.cpp:

const String keys =
    "{d display |1  | view video while objects are detected}"
    "{t tensorrt|false | use tensorrt}"
    "{i int8|false| use INT8 (requires callibration)}"
    "{v video    |  | video for detection}"
    "{graph ||frozen graph location}"
    "{labels ||trained labels filelocation}";

Examples are in run_*.sh files in the sources directory. Worth mentioning:

-d=0 - run without UX, print out framerate only. -d=2 run with UX
-t - TensorRT graph
-t -i - TensorRT graph with INT8 precision.

Slowdown due to UX

The application uses a bare-bones OpenCV UI for visual feedback (imshow) and that causes a significant perf hit, so to measure actual performance we run with -d=0 which suppresses the UI.