287,751 research outputs found
A Biologically Motivated Software Retina for Robotic Sensors for ARM-Based Mobile Platform Technology
A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the smartphone is so exquisitely integrated by having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to childrenās toys, such as the Meccanoid robot [5], employ a smartphone to provide a vision system/control system [7,8].
Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance. For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels.
In our research we have been developing biologically motivated foveated vision algorithms based on a model of the mammalian retina [2], potentially 100 times more efficient than their conventional counterparts. Accordingly, vision systems based on the foveated architectures found in mammals have also the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture [2,3,4] that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage. Given the above processing/bandwidth limitations, a viable way forward would be to perform off-line learning and implement the forward recognition path on the mobile platform, returning simple object labels, or sparse hierarchical feature symbols, and gaze control commands to the host robot vision system and controller.
We are now at the early stages of investigating how best to port our foveated architecture onto an ARM-based smartphone platform. To achieve the required levels of performance we propose to port and optimise our retina model to the mobile ARM processor architecture in conjunction with their integrated GPUs. We will then be in the position to provide a foveated smart vision system on a smartphone with the advantage of processing speed gains and bandwidth optimisations. Our approach will be to develop efficient parallelising compilers and perhaps propose new processor architectural features to support this approach to computer vision, e.g. efficient processing of hexagonally sampled foveated images.
Our current goal is to have a foveated system running in real-time on at least a 1080p input video stream to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone. Initially this system would communicate a symbol stream to conventional hardware performing back-end visual classification/interpretation, although simple object detection and recognition tasks should be possible on-board the device. We propose that, as in Nature, foveated vision is the key to achieving the necessary data reduction to be able to implement complete visual recognition and learning processes on the smartphone itself
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework
We propose a Bayesian evidence framework to facilitate transfer learning from
pre-trained deep convolutional neural networks (CNNs). Our framework is
formulated on top of a least squares SVM (LS-SVM) classifier, which is simple
and fast in both training and testing, and achieves competitive performance in
practice. The regularization parameters in LS-SVM is estimated automatically
without grid search and cross-validation by maximizing evidence, which is a
useful measure to select the best performing CNN out of multiple candidates for
transfer learning; the evidence is optimized efficiently by employing Aitken's
delta-squared process, which accelerates convergence of fixed point update. The
proposed Bayesian evidence framework also provides a good solution to identify
the best ensemble of heterogeneous CNNs through a greedy algorithm. Our
Bayesian evidence framework for transfer learning is tested on 12 visual
recognition datasets and illustrates the state-of-the-art performance
consistently in terms of prediction accuracy and modeling efficiency.Comment: Appearing in CVPR-2016 (oral presentation
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-truth object masks from the instance-level object segmentation
network training, and then enhance the detection network with top-down
segmentation feedbacks. The pseudo ground truth mask and network parameters are
optimized alternatively to mutually benefit each other. To obtain the promising
pseudo masks in each iteration, we embed a graphical inference that
incorporates the low-level image appearance consistency and the bounding box
annotations to refine the segmentation masks predicted by the segmentation
network. Our approach progressively improves the object detection performance
by incorporating the detailed pixel-wise information learned from the
weakly-supervised segmentation network. Extensive evaluation on the detection
task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is
effective
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an end-to-end learning and strongly reduces the training time. The
technique is based on a family of differentiable pruning functions and a new
regularizer specifically designed to enforce pruning. The experimental results
show that the joint optimization of both the thresholds and the network weights
permits to reach a higher compression rate, reducing the number of weights of
the pruned network by a further 14% to 33% compared to the current
state-of-the-art. Furthermore, we believe that this is the first study where
the generalization capabilities in transfer learning tasks of the features
extracted by a pruned network are analyzed. To achieve this goal, we show that
the representations learned using the proposed pruning methodology maintain the
same effectiveness and generality of those learned by the corresponding
non-compressed network on a set of different recognition tasks.Comment: 8 pages, 5 figures. Published as a conference paper at ICPR 201
- ā¦