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diff --git a/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cameras.jpg b/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cameras.jpg
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--- a/site/content/pages/datasets/duke_mtmc/assets/duke_mtmc_cameras.jpg
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diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md
index aa5ce5e5..ceed1416 100644
--- a/site/content/pages/datasets/duke_mtmc/index.md
+++ b/site/content/pages/datasets/duke_mtmc/index.md
@@ -22,7 +22,7 @@ The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of vide
 
 The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras.[^duke_mtmc_orig] It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations. 
 
-![caption: A collection of 1,600 out of the 2,700 students captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. &copy; megapixels.cc](assets/duke_mtmc_reid_montage.jpg)
+![caption: A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. &copy; megapixels.cc](assets/duke_mtmc_reid_montage.jpg)
 
 The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation[^duke_mtmc_orig]. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense[^cn_defense1][^cn_defense2], IARPA and IBM[^iarpa_ibm], and U.S. Department of Homeland Security[^us_dhs].
 
@@ -30,7 +30,6 @@ The 8 cameras deployed on Duke's campus were specifically setup to capture stude
 
 ![caption: Duke MTMC camera locations on Duke University campus &copy; megapixels.cc](assets/duke_mtmc_camera_map.jpg)
 
-
 ![caption: Duke MTMC camera views for 8 cameras deployed on campus &copy; megapixels.cc](assets/duke_mtmc_cameras.jpg)
 
 ![caption: Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus &copy; megapixels.cc](assets/duke_mtmc_saliencies.jpg)
@@ -52,8 +51,7 @@ The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth fi
 [^sensenets_sensetime]: "Attention-Aware Compositional Network for Person Re-identification". 2018. [Source](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e)
 [^sensetime1]: "End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. [source](https://www.semanticscholar.org/paper/End-to-End-Deep-Kronecker-Product-Matching-for-Shen-Xiao/947954cafdefd471b75da8c3bb4c21b9e6d57838)
 [^sensetime2]: "Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. [Source](https://www.semanticscholar.org/paper/Person-Re-identification-with-Deep-Graph-Neural-Shen-Li/08d2a558ea2deb117dd8066e864612bf2899905b)
-[^duke_mtmc_orig]: "Performance Measures and a Data Set for
-Multi-Target, Multi-Camera Tracking". 2016. [Source](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c)
+[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [Source](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c)
 [^cn_defense1]: "Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. [Source](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786)
 [^cn_defense2]: "Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. [Source](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881)
 [^iarpa_ibm]: "Horizontal Pyramid Matching for Person Re-identification". 2019. [Source](https://www.semanticscholar.org/paper/Horizontal-Pyramid-Matching-for-Person-Fu-Wei/c2a5f27d97744bc1f96d7e1074395749e3c59bc8)