From 695ef5d20a22c91252e4b6dffbbb9a9511ae2a93 Mon Sep 17 00:00:00 2001
From: Leon Lok <44379813+LeonLok@users.noreply.github.com>
Date: Mon, 15 Jun 2020 15:59:26 +0100
Subject: [PATCH] updated link

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c537fe6..a1fb977 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ This project was inspired by:
 I swapped out YOLO v3 for YOLO v4 and added the option for asynchronous processing, which significantly improves
 the FPS. However, FPS monitoring is disabled when asynchronous processing is used since it isn't accurate.
 
-In addition, I took the algorithm from this [paper](https://www.researchgate.net/publication/337541842_Vehicle_Tracking_Using_Deep_SORT_with_Low_Confidence_Track_Filtering) and implemented it into `deep_sort/track.py`.
+In addition, I took the algorithm from this [paper](https://ieeexplore.ieee.org/document/8909903) and implemented it into `deep_sort/track.py`.
 The original method for confirming tracks was based simply on the number of times an object has been detected without considering detection confidence, leading to high tracking false positive rates when unreliable detections occur (i.e. low confidence true positives or high confidence false positives). The track filtering algorithm reduces this significantly by calculating the average detection confidence over a set number of detections before confirming a track.
 
 See the comparison video below.