Answers.Body: <ol>
  <li>Model architecture:</li>
  </ol>
  <p>In machine learning, static image detectors can be is very different from video detectors, as movement plays a big role on the task. So, even when comparing frames the objects are similar, when digesting the video, a model can learn very different things. Maybe adding parked cars to the database increased false positives, mistakenly labeling other static noises as cars.</p>
  <ol start="2">
  <li>Business goal:</li>
  </ol>
  <p>Why are you labeling cars from security camera, in the first place? What is your goal here? Maybe if you want to know car density in a parking lot, than labeling parked cars is very useful. But if you just need to know traffic <strong>flow</strong>, than parked cars will be just a distraction, noise from the data.
  So maybe whoever built the dataset had a different goal in mind.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-18T10:44:52.297Z
Answers.Text: On  further reflection, I noticed that there is a black mask aviable covering area of "no interest", including parked cars. I'm going to use something similar: the user select the "important" area and the model will detect all cars inside.
  I just need some YOLO behavior, so my business goal is to find all cars. 
  However, including parked ones may over-represent one red Ferrari that will be labeled in all frames.
Answers.UserDisplayName: Francesco Pagani
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-17T22:20:39.133Z
Answers.LastActivityDate: 2021-08-17T22:20:39.133Z
Answers.UserDisplayName: Andre Goulart
Body: <p>I'm using nvidia Transfer Learning Toolkit to detect cars in some video frames.</p>
  <p>I found some dataset (for example <a href="https://www.jpjodoin.com/urbantracker/dataset.html" rel="nofollow noreferrer">https://www.jpjodoin.com/urbantracker/dataset.html</a> and <a href="https://www.kaggle.com/aalborguniversity/aau-rainsnow" rel="nofollow noreferrer">https://www.kaggle.com/aalborguniversity/aau-rainsnow</a>) and I noticed that usually parked cars are not labeled, and covered under a mask.</p>
  <p>Why shouldn't I add also their labels? It would be easy to label them because they are static objects and I could copy-paste in all labels set. So why in video dataset they are not labelled?</p>
  
Tags: <image-recognition><image-segmentation><transfer-learning><yolo><labels>
Title: Should I label static objects on video dataset?
UserDisplayName: Francesco Pagani
ThreadText: # Should I label static objects on video dataset?
  
  (Asked by: username_0 on 2021-08-17)
  
  I'm using nvidia Transfer Learning Toolkit to detect cars in some video frames.
  
  I found some dataset (for example <https://www.jpjodoin.com/urbantracker/dataset.html> and <https://www.kaggle.com/aalborguniversity/aau-rainsnow>) and I noticed that usually parked cars are not labeled, and covered under a mask.
  
  Why shouldn't I add also their labels? It would be easy to label them because they are static objects and I could copy-paste in all labels set. So why in video dataset they are not labelled?
  
  **Tags:** image-recognition, image-segmentation, transfer-learning, yolo, labels
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-08-17. Score: 1
  
  1. Model architecture:
  
  In machine learning, static image detectors can be is very different from video detectors, as movement plays a big role on the task. So, even when comparing frames the objects are similar, when digesting the video, a model can learn very different things. Maybe adding parked cars to the database increased false positives, mistakenly labeling other static noises as cars.
  
  2. Business goal:
  
  Why are you labeling cars from security camera, in the first place? What is your goal here? Maybe if you want to know car density in a parking lot, than labeling parked cars is very useful. But if you just need to know traffic **flow**, than parked cars will be just a distraction, noise from the data.
  So maybe whoever built the dataset had a different goal in mind.
  
  > **Comments:**
  > **username_0**: On  further reflection, I noticed that there is a black mask aviable covering area of "no interest", including parked cars. I'm going to use something similar: the user select the "important" area and the model will detect all cars inside. I just need some YOLO behavior, so my business goal is to find all cars.  However, including parked ones may over-represent one red Ferrari that will be labeled in all frames.  
  
  ---
  
  