Answers.Body: <p>You're facing a common challenge in action recognition tasks, especially when moving from controlled pre-recorded datasets to real-time video streams with variable conditions. Applying background segmentation could be an effective strategy, but there are several factors to consider. Let's break down your questions:</p>
  <p><strong>1. Would Applying Background Segmentation Improve Performance?</strong></p>
  <p><strong>Potential Benefits:</strong></p>
  <p>=&gt; Reduced Noise: By removing irrelevant background information, the model can focus solely on the human-object interaction, which could improve action detection accuracy.</p>
  <p>=&gt; Better Generalization: Segmenting out the background may help reduce the model’s reliance on spurious correlations (like background features) learned from the training set. This could improve performance on real-time streams that have unseen backgrounds or environmental variations.</p>
  <p><strong>Drawbacks/Challenges:</strong></p>
  <p>=&gt; Loss of Context: Some actions might depend on the broader environmental context (e.g., &quot;moving through a door&quot; or &quot;operating a machine in a specific area&quot;). Segmenting out too much background could lead to a loss of essential spatial information that contributes to action recognition.</p>
  <p>=&gt; Increased Complexity: Incorporating a segmentation step could introduce additional latency, especially for real-time applications. YOLO is fast, but the combined pipeline may not meet real-time performance constraints depending on hardware and implementation.</p>
  <p>=&gt; Misalignment of Features: I3D models are designed to capture spatial-temporal patterns. If the segmentation is imperfect or noisy, it might cut off parts of the human or object, distorting spatial patterns crucial for action recognition.</p>
  <p><strong>2. Challenges of Using Segmentation with I3D (3D CNN-based Models):</strong></p>
  <p>=&gt; Temporal Coherence: I3D relies heavily on temporal information across frames. Background segmentation applied to each frame individually could disrupt temporal coherence, especially if the segmentation is inconsistent across frames (e.g., sudden flickering in background segmentation).
  Action Context Loss: Certain actions might involve interactions with the environment that aren’t explicitly tied to the person or object. Removing too much context may reduce the model's ability to recognize the full action sequence, especially in industrial settings where equipment or surroundings matter.</p>
  <p>=&gt; Motion Discrepancies: Segmentation models like YOLO may not always accurately capture motion in fast-changing real-time scenarios. This could lead to mismatches between the segmented object and the action being performed.</p>
  <p><strong>3. Better Alternatives to Improve Real-Time Action Recognition:</strong></p>
  <p>=&gt; Use Optical Flow for Temporal Dynamics: Instead of relying purely on spatial segmentation, consider using optical flow to capture motion between frames. Optical flow is robust to background noise and focuses on movement, which might help your I3D model perform better in real-time streams.
  Domain Adaptation Techniques: Since your real-time stream environment differs from the training data, you could apply domain adaptation techniques (like adversarial training or style transfer) to help your model generalize better to unseen scenarios with different lighting and camera angles.
  Real-Time Data Augmentation: Implement real-time augmentation techniques, such as adding noise, changing lighting conditions, or shifting the camera angle during training to help your model handle diverse real-world situations more effectively.</p>
  <p>=&gt; Attention Mechanisms: Introduce an attention mechanism in your I3D model to focus on relevant parts of the frame automatically. This could allow the model to prioritize human-object interactions without explicitly needing background segmentation.</p>
  <p>=&gt; Hybrid Approach (Segmentation + Full Frame): Instead of removing the background entirely, consider a hybrid approach where the segmentation output is used as an additional input channel to guide the I3D model on where to focus. This way, the model retains full-frame information while emphasizing human-object interactions.</p>
  <p>=&gt; One more method to recommend which I have used for training action recognition models is using the CNN+LSTM+Attension model to train the action recognition model on the short video like some short clip of particular action or some thing like that it would be great i have trained model like this one and it worked well for me</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-25T04:13:21.633Z
Answers.LastActivityDate: 2024-09-25T04:13:21.633Z
Answers.UserDisplayName: Keval Pandya
Body: <p>I am working on an action recognition task involving human-object interactions using an <a href="https://arxiv.org/pdf/1705.07750" rel="nofollow noreferrer">I3D</a> (3D CNN-based) model. The model was trained on pre-recorded videos, and it performed well during evaluation. However, when I applied it to unseen real-time stream videos, it struggled to detect actions accurately.</p>
  <p><strong>The key issues I observed:</strong></p>
  <ol>
  <li>The model was <strong>failing to detect actions</strong> in real-time video streams.</li>
  <li>Even worse, it classified actions in frames where <strong>neither the person nor the object of interest was present</strong>.</li>
  </ol>
  <p>I suspect that the presence of irrelevant background information in real-time streams might be confusing the model. To address this, I am considering applying a segmentation model (e.g., YOLO for segmentation) to remove the background and retain only the human and the object interacting.</p>
  <p><strong>My questions are:</strong></p>
  <ol>
  <li>Would applying <strong>background segmentation</strong> improve the action recognition performance, particularly in real-time human-object
  interactions?</li>
  <li><strong>What are the potential drawbacks or challenges of using segmentation with action recognition model</strong>, which is designed to capture spatial-temporal patterns?</li>
  <li><strong>Are there better alternatives for</strong> improving action recognition performance on real-time streams,
  especially for unseen scenarios?</li>
  </ol>
  <p><strong>Additional Context:</strong></p>
  <ol>
  <li>The I3D model performed well on pre-recorded datasets but fails on real-time streams with complex backgrounds.</li>
  <li>I am currently using YOLO segmentation to detect humans and objects, but I haven’t yet integrated this with the action recognition pipeline.</li>
  <li>The dataset and real-time stream involve industrial settings with variable lighting and camera angles.</li>
  </ol>
  
Tags: <computer-vision><image-segmentation><real-time><video-classification><action-recognition>
Title: Is background segmentation effective for improving action recognition model on real-time human-object interaction videos?
UserDisplayName: Renat Abdrakhmanov
ThreadText: # Is background segmentation effective for improving action recognition model on real-time human-object interaction videos?
  
  (Asked by: username_0 on 2024-09-24)
  
  I am working on an action recognition task involving human-object interactions using an [I3D](https://arxiv.org/pdf/1705.07750) (3D CNN-based) model. The model was trained on pre-recorded videos, and it performed well during evaluation. However, when I applied it to unseen real-time stream videos, it struggled to detect actions accurately.
  
  **The key issues I observed:**
  
  1. The model was **failing to detect actions** in real-time video streams.
  2. Even worse, it classified actions in frames where **neither the person nor the object of interest was present**.
  
  I suspect that the presence of irrelevant background information in real-time streams might be confusing the model. To address this, I am considering applying a segmentation model (e.g., YOLO for segmentation) to remove the background and retain only the human and the object interacting.
  
  **My questions are:**
  
  1. Would applying **background segmentation** improve the action recognition performance, particularly in real-time human-object
     interactions?
  2. **What are the potential drawbacks or challenges of using segmentation with action recognition model**, which is designed to capture spatial-temporal patterns?
  3. **Are there better alternatives for** improving action recognition performance on real-time streams,
     especially for unseen scenarios?
  
  **Additional Context:**
  
  1. The I3D model performed well on pre-recorded datasets but fails on real-time streams with complex backgrounds.
  2. I am currently using YOLO segmentation to detect humans and objects, but I haven’t yet integrated this with the action recognition pipeline.
  3. The dataset and real-time stream involve industrial settings with variable lighting and camera angles.
  
  **Tags:** computer-vision, image-segmentation, real-time, video-classification, action-recognition
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-09-25. Score: 0
  
  You're facing a common challenge in action recognition tasks, especially when moving from controlled pre-recorded datasets to real-time video streams with variable conditions. Applying background segmentation could be an effective strategy, but there are several factors to consider. Let's break down your questions:
  
  **1. Would Applying Background Segmentation Improve Performance?**
  
  **Potential Benefits:**
  
  => Reduced Noise: By removing irrelevant background information, the model can focus solely on the human-object interaction, which could improve action detection accuracy.
  
  => Better Generalization: Segmenting out the background may help reduce the model’s reliance on spurious correlations (like background features) learned from the training set. This could improve performance on real-time streams that have unseen backgrounds or environmental variations.
  
  **Drawbacks/Challenges:**
  
  => Loss of Context: Some actions might depend on the broader environmental context (e.g., "moving through a door" or "operating a machine in a specific area"). Segmenting out too much background could lead to a loss of essential spatial information that contributes to action recognition.
  
  => Increased Complexity: Incorporating a segmentation step could introduce additional latency, especially for real-time applications. YOLO is fast, but the combined pipeline may not meet real-time performance constraints depending on hardware and implementation.
  
  => Misalignment of Features: I3D models are designed to capture spatial-temporal patterns. If the segmentation is imperfect or noisy, it might cut off parts of the human or object, distorting spatial patterns crucial for action recognition.
  
  **2. Challenges of Using Segmentation with I3D (3D CNN-based Models):**
  
  => Temporal Coherence: I3D relies heavily on temporal information across frames. Background segmentation applied to each frame individually could disrupt temporal coherence, especially if the segmentation is inconsistent across frames (e.g., sudden flickering in background segmentation).
  Action Context Loss: Certain actions might involve interactions with the environment that aren’t explicitly tied to the person or object. Removing too much context may reduce the model's ability to recognize the full action sequence, especially in industrial settings where equipment or surroundings matter.
  
  => Motion Discrepancies: Segmentation models like YOLO may not always accurately capture motion in fast-changing real-time scenarios. This could lead to mismatches between the segmented object and the action being performed.
  
  **3. Better Alternatives to Improve Real-Time Action Recognition:**
  
  => Use Optical Flow for Temporal Dynamics: Instead of relying purely on spatial segmentation, consider using optical flow to capture motion between frames. Optical flow is robust to background noise and focuses on movement, which might help your I3D model perform better in real-time streams.
  Domain Adaptation Techniques: Since your real-time stream environment differs from the training data, you could apply domain adaptation techniques (like adversarial training or style transfer) to help your model generalize better to unseen scenarios with different lighting and camera angles.
  Real-Time Data Augmentation: Implement real-time augmentation techniques, such as adding noise, changing lighting conditions, or shifting the camera angle during training to help your model handle diverse real-world situations more effectively.
  
  => Attention Mechanisms: Introduce an attention mechanism in your I3D model to focus on relevant parts of the frame automatically. This could allow the model to prioritize human-object interactions without explicitly needing background segmentation.
  
  => Hybrid Approach (Segmentation + Full Frame): Instead of removing the background entirely, consider a hybrid approach where the segmentation output is used as an additional input channel to guide the I3D model on where to focus. This way, the model retains full-frame information while emphasizing human-object interactions.
  
  => One more method to recommend which I have used for training action recognition models is using the CNN+LSTM+Attension model to train the action recognition model on the short video like some short clip of particular action or some thing like that it would be great i have trained model like this one and it worked well for me
  
  
  ---
  
  