From e6d1a98dd71338dd2c74868066704d10453e6f11 Mon Sep 17 00:00:00 2001 From: Nicolai Wojke Date: Wed, 15 Feb 2017 09:13:12 +0100 Subject: [PATCH] Some more README fixes --- README.md | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 3f712638..42667b78 100644 --- a/README.md +++ b/README.md @@ -9,16 +9,17 @@ This repository contains code for *Simple Online and Realtime Tracking with a De Tracking code: * NumPy -* sklean +* sklean (for linear assignment) +* OpenCV (for visualization) -Additional, feature generation requires: +Additionally, feature generation requires: * TensorFlow * tfslim ## Installation -First, clone the Git repository: +First, clone the repository: ``` git clone https://github.com/nwojke/deep_sort.git ``` @@ -31,7 +32,7 @@ The following example starts the tracker on one of the [MOT16 benchmark](https://motchallenge.net/data/MOT16/) sequences. We assume resources have been extracted to the repository root directory and -the MOT16 benchmark data resides in `./MOT16`: +the MOT16 benchmark data is in `./MOT16`: ``` python deep_sort_app.py \ --sequence_dir=./MOT16/test/MOT16-06 @@ -40,21 +41,29 @@ python deep_sort_app.py \ --nn_budget=100 \ --display=True ``` +Check `python deep_sort_app.py -h` for an overview of available options. +There are also scripts in the repository to visualize results, generate videos, +and evaluate the MOT challenge benchmark. -## Generating detections. +## Generating detections -This respository contains a script to generate features for person -re-identification, suitable to compare the visual appearance of detector -bounding boxes using cosine similarity. +Beside the main tracking application, this repository contains a script to +generate features for person re-identification, suitable to compare the visual +appearance of pedestrian bounding boxes using cosine similarity. The following example generates these features from standard MOT challenge detections. Again, we assume resources have been extracted to the repository -root directory and MOT16 data resides in `./MOT16`: +root directory and MOT16 data is in `./MOT16`: ``` python generate_detections.npy --model=resources/networks/mars-small128.ckpt \ - --mot_dir=./MOT16/train + --mot_dir=./MOT16/train \ --output_dir=./resources/detections/MOT16_train ``` +For each sequence of the MOT16 dataset, the output is stored as separate binary +file in NumPy native format. Each file contains an array of shape `Nx138`, +where N is the number of detections in the corresponding MOT sequence. +The first 10 columns of this array contain the raw MOT detection copied over +from the input file. The remaining 128 columns store the appearance descriptor. ## Highlevel overview of source files