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Real-Time Modelling Visual
Scenes with Biological
Sheffield Hallam University
AI now and before
• Computer Vision and natural language processing have improved
significantly over the past 10 years.
• Image recognition and classification systems
• Apple photo organiser, Facebook face recognition.
• Robot use in warehouse
• Amazon warehouse robots (https://www.youtube.com/watch?v=4sEVX4mPuto)
• Medical image analysis for healthcare
• non-invasive diagnosis
• Agriculture, sport, manufacturing, autonomous cars technology.
• Crop yield, goal-line technology, defective products, people detection.
Human level face recognition Taigman et. al. CVPR2014
Why AI acceleration
• Better algorithms that learn from examples not predefined rules
• Deep learning
• Neural networks
• Machine perception
• Availability of data – Big Data
• Internet images, YouTube videos, Facebook images
• High Performance Computing
• Field Programmable Gate Arrays (FPGAs)
• Graphics Processing Units (GPU)
• Visual recognition with high accuracies.
• 3D reconstruction of an environment
Mask R-CNN He et. al. ICCV2017
Litjens et. al. 2017
Johnson et. al. CVPR2015
Driverless cars - Mathworks
Faster R-CNN TPAMI 2017
Where things fall apart
• March 18, 2018, Uber’s autonomous car hit and killed 49-year-old as
she was walking her bike across the street.
• Novel and imperfect system
• March 23 2018, autopilot Tesla slammed into concrete killing driver.
• Security robots attacking a kid in a shopping area, July 2016.
• Robot failure to open different doors – which training mode.
• Reinforced learning
• Supervised or Unsupervised?
Why things go wrong
• For autonomous cars, the state of the art is good and providing
bounding boxes of objects in the scene.
• What is missing is an interpretation of the scene.
• No contextual reasoning.
• Robot navigation
• Decision making might be optimal but not feasible or safe.
• Modelling in a crowded scene to infer interaction
• Modelling very unusual situations with little or no data
• Things that human are capable of, e.g. dealing with complex scenes
Unsupervised Background Subtraction
• Image Segmentation separate moving
objects from the background.
• Background subtraction is a practical
approach when the image sensor is
• Background Modelling techniques
W4 and Grimson’s Algorithm – 2000s
• Requires manual initialization of
the Maximum (M), Minimum (m)
& inter-frame difference (D)
• Pixel x of image I is foreground if
|m(x)-It(x)|>D(x) or |M(x)-
• Detection, Motion & change
history maps used for outdoor
• Use of fixed-point update values.
• Bimodal can’t model problems like
moving foliage and lighting
• Mixture of Gaussians with
associated weights to model each
• Parameters are updated as follows:
• The first B distributions, ordered
by weight represents the
• Robust in modeling multimodal
• Suffers from blending effect and
uses floating point in all updates
Efficient Hardware Implementation
• Maintains K clusters each with weight wk, central value ck
and implied global range [ck-15, ck+15]
• Weights and central values of all clusters are initialized 0,
and updated as follows:
• Uses both pixel and frame-level processing
• The first B distributions, ordered by weight represents the
Appiah et al FPT 2005
TULIPP – The game changer!
• Tools to help real-time computer vision developer to focus on:
• core application development by automating recurring, but critical,
tasks such as performance instrumentation
• Design space exploration and
• Vendor tool configuration.
• Making it possible for the designer to get the required
performance in speed, coupled with power constraints without
having to worry too much about the architecture.
Imaging before Deep Learning
• Standard feature detectors
• SIFT, HOG, LBP
• Different algorithms for object
• Requires small amount of data
• Useful for measurement and
• Featured are learnt and stacked
according to data
• Same algorithm that adapts to the
• Requires huge volume of data
• Useful for labelling
MathworksDalal & Triggs cc.gatech.edu
Deep CNN – Overview
• Uses convolution to preserve the spatial
structure of the input image
• Instead of a sigmoid activation function,
ReLU (rectified linear unit) is often used
• Encourages sparsity of synapses as
the value approaches zero (0).
Credit : Fei-Fei Li CS231n; Bala Amavasai – IEEE & M. Turner
Feature Maps - Several feature maps are used to identify various local features
• Several feature maps are used to
identify various local features.
• Each convolution filter can be tuned
to edges of different
• Orientation, Frequency, Phase, Colour, etc
• Capture some aspects of neural response
• But neural data not used in training
Sparse local connectivity
• For an input image of size 7x7
• The convolution filter 3x3
• The output image will be 5x5
• (Image – Filter )/stride + 1
• A sample filter for horizontal and
• Computer Vision meets Cognitive Science and Neuroscience
Fei-Fei Li & Justin Johnson & Serena Yeung
• The success stories about the rise of Convolutional Neural Networks
(CNNs) capable of learning high-level features in object recognition
• due to the availability of large datasets like ImageNet
• However, performance at scene recognition has not attained the same
level of success.
• Yet large scene databases like SUN and Places do exist
• Maybe the current deep features trained from ImageNet are not
competitive enough for such tasks.
• But do primates and humans actually do a raster scan to understand a
• CNNs fail to capture insensitivity to perturbations of an image
• Performance accuracies in CNNs relies on a huge search space.
• The need for more biological guidance from the visual cortex
• Multi-disciplinary research in neuroscience, psychology,
physiology, shows that:
• object recognition in visual cortex is modulated via the ventral stream
• Neuronal signals from the retina are transformed into high-level
representation for object recognition.
• Computer Scientist working with neuroscientist, psychologist,
etc. would have better models for understanding scenes.
• A biologically Inspired Deep CNN Model [Zhang et al. 2016]
• Simulates the V1, V2, V4 and IT layers of the human ventral stream
• Uses convolutional layers with varied sizes and complexities
• Increased concurrency for improved processing speed
• Outperformed seven other CNN techniques using four datasets.
• You Only Look Once (YOLOv2) [Redmon and Farhadi CVPR2017]
• Based on the assumption that humans glance at an image
• Does not rely on sliding window like other deep learning approaches
• Outperforms Deformable Part Models (DPM) and Regional CNN.
Scene understanding with DNN
• Learning Deep Features for Scene Recognition using Places
Database [Zhou et al. NIPS2014]
• Uses CNN to learn features from the scene
• Combined various local and global features to understand the scene
• Presents scene categories where machines perform like humans.
• Humans, but Not Deep Neural Networks, Often Miss Giant
Targets in Scenes [Eckstein et al. Current Biology 2017]
• Humans often miss unusual sized targets during visual search
• Deep learning does not exhibit such deficit with targets
• Is that a good thing or not?
• Missing giant targets is a functional brain strategy to discount
Eckstein et al. Current Biology 2017
• To understand how humans and primates recognise scenes
• Provide them with samples of indoor scenes
• Ask them to identify specific objects
• Observe their recall mechanism, if spatial relationship plays a role
• Model the scene to account for the experimental results
• Incorporate global and local descriptors
• Construct a relationship vector
Lunchroom image : PASSTA Dataset
• Computer vision and machine learning have improved over the
years, thanks to more data and processing power.
• Global scene understanding is still a challenge.
• Multi-disciplinary effort required to take computer vision to the
next level, acceptable for applications like driverless cars.
• We aim to combine positives of CNN with what humans are
good at for scene understanding.
• TULIPP offers the platform with toolchain to drive this agenda.