Action & Activity Recognition

Introduction

Motivation

• Gain a higher level understanding of the scene, e.g.

  • What are these persons doing (walking, sitting, working, hiding)?
  • How are they doing it?
  • What is going on in the scene (meeting, party, telephone conversation, etc…)?

• Applications

  • video indexing/analysis,
  • smart-rooms,
  • patient monitoring,
  • surveillance,
  • robots etc.

Actions, Activities

Event

• “a thing that happens or takes place”
• Examples
  • Gestures
  • Actions (running, drinking, standing up, etc.)
  • Activities (preparing a meal, playing a game, etc.)
  • Nature event (fire, storm, earthquake, etc.)
  • …

Human actions

• Def 1: Physical body motion

  • E.g.: Walking, boxing, clapping, bending, …

• Def 2: Interaction with environment on specific purpose

  • E.g.

    

Activities

• Complex sequence of action,
• Possibly performed by multiple humans,
• Typically longer temporal duration
• Examples
  • Preparing a meal
  • Having a meeting
  • Shaking hands
  • Football team scoring a goal

Actions / Activity Hierarchy

Example: Small groups (meetings)

• Individual actions: Speaking, writing, listening, walking, standing up, sitting down, “fidgeting”,…
• Group activities: Meeting start, end, discussion, presentation, monologue, dialogue, white board, note-taking
• Often audio-visual cues

Approaches

• Time series classification problem similar to speech/gesture recognition

  • Typical classifiers:
    • HMMs and variants (e.g. Coupled HMMs, Layered HMMs) Dynamic
    • Bayesian Networks (DBN)
    • Recurrent neural networks

• Classification problem similar to object recognition/detection

  • Typical classifiers:
    • Template matching
    • Boosting
    • Bag-of-Words SVMs
  • Deep Learning approaches:
    • 2D CNN (e.g. Two-Stream CNN, Temporal Segment Network)
    • 3D CNN (e.g. C3D, I3D)
    • LSTM on top of 2D/3D CNN

Recognition with local feature descriptors

• Try to model both Space and Time
  • Combine spatial and motion descriptors to model an action
• Action == Space-time objects
  • Transfer object detectors to action recognition

Space-Time Features + Boosting

💡 Idea

• Extract many features describing the relevant content of an image sequence
  • Histogram of oriented gradients (HOG) to describe appearance
  • Histogram of oriented flow (HOF) to describe motion in video
• Use Boosting to select and combine good features for classification

Action features

• Action volume = space-time cuboid region around the head (duration of action)

• Encoded with block-histogram features $f\_{\theta}(\cdot)$

  $$\theta=(x, y, t, d x, d y, d t, \beta, \varphi)$$
  • Location: $(x, y, t)$
  • Space-time extent: $(d x, d y, d t)$
  • Type of block: $\beta \in \\{\text{Plain, Temp-2, Spat-4}\\}$
  • Type of histogram: $\varphi$
    • Histogram of optical flow (HOF)
    • Histogram of oriented gradient (HOG)

• Example

  

Histogram features

• (simplified) Histogram of oriented gradient (HOG)
  • Apply gradient operator to each frame within sequence (eg. Sobel)
  • Bin gradients discretized in 4 orientations to block-histogram
• Histogram of optical flow (HOF)
  • Calculate optical flow (OF) between frames
  • Bin OF vectors discretized in 4 direction bins (+1 bin for no motion) to block-histogram
• Normalized action cuboid has size 14x14x8 with units corresponding to 5x5x5 pixels

Action Learning

• Use boosting method (eg. AdaBoost) to classify features within an action volume
• Features: Block-histogram features

Boosting

• A weak classifier h is a classifier with accuracy only slightly better than chance

• Boosting combines a number of weak classifiers so that the ensemble is arbitrarily accurate

  

  • Allows the use of simple (weak) classifiers without the loss if accuracy
  • Selects features and trains the classifier

Space-Time Interest Points (STIP) + Bag-of-Words (BoW)

Inspired by Bag-of-Words (BoW) model for object classification

Bag-of-Words (BoW) model

• “Visual Word“ vocabulary learning

  • Cluster local features

  • Visual Words = Cluster Means

• BoW feature calculation

  • Assign each local feature most similar visual word

  • BoW feature = Histogram of visual word occurances within a region

• Histogram can be used to classify objects (wth. SVM)

Bag of Visual Words (Stanford CS231 slides)

1. Feature detection and representation

   

2. Codewords dictionary formation

   

3. Bag of word representation

   

Space-Time Features: Detector

Space-Time Interest Points (STIP)

Space-Time Extension of Harris Operator

• Space-Time Extension of Harris Operator
  • Add dimensionality of time to the second moment matrix
  • Look for maxima in extended Harris corner function H
• Detection depends on spatio-temporal scale
• Extract features at multiple levels of spatio-temporal scales (dense scale sampling)

Space-Time Features: Descriptor

Compute histogram descriptors of space-time volumes in neighborhood of detected points

• Compute a 4-bin HOG for each cube in 3x3x2 space-time grid
• Compute a 5-bin HOF for each cube in 3x3x2 space-time grid

Action classification

• Spatio-temporal Bag-of-Words (BoW)

  • Build Visual vocabulary of local feature representations using k-means clustering

  • Assign each feature in a video to nearest vocabulary word

  • Compute histogram of visual word occurrences over space time volume of a video squence

• SVM classification

  • Combine different feature types using multichannel $\chi^{2}$ Kernel
  • One-against-all approach in case of multi-class classification

Dense Trajectories ¹

• Dense sampling improves results over sparse interest points for image classification
• The 2D space domain and 1D time domain in videos have very different characteristics $\rightarrow$ use them both

Feature trajectories

• Efficient for representing videos
  • Extracted using KLT tracker or matching SIFT descriptors between frames
  • However, the quantity and quality is generally not enough 🤪
• State-of-the-art: The state of the art now describe videos by dense trajectories

Dense Trajectories

• Obtain trajectories by optical flow tracking on densely sampled points

  • Sampling
    • Sample features points every 5th pixel
    • Remove untrackable points (structure / Eigenvalue analysis)
    • Sample points on eight different scales
  • Tracking
    • Tracking by median filtering in the OF-Field
    • Trajectory length is fixed (e.g. 15 frames)

• Feature tracking

  • Points of subsequent frames are concatenated to form a trajectory

  • Trajectories are limited to $L$ frames in order to avoid drift from their initial location

  • The shape of a trajectory of length $L$ is described by the sequence

    $$S=\left(\Delta P\_{t}, \ldots, \Delta P\_{t+L-1}\right)$$

  • The resulting vector is normalized by

    $$\begin{array}{c} \Delta P\_{t}=\left(P\_{t+1}-P\_{t}\right)=\left(x\_{t+1}-x\_{t}, y\_{t+1}-y\_{t}\right) \\\\ S^{\prime}=\frac{\left(\Delta P\_{t}, \ldots, \Delta P\_{t+L-1}\right)}{\sum\_{j=t}^{t+L-1}\left\|\Delta P\_{j}\right\|} \end{array}$$

Trajectory descriptors

• Histogram of Oriented Gradient (HOG)

• Histogram of Optical Flow (HOF)

• HOGHOF

• Motion Boundary Histogram (MBH)

  • Take local gradients of x-y flow components and compute HOG as in static images