Rotational Invariant Object Recognition for Robotic Vision

Vilar, Cristian; Krug, Silvia; Thörnberg, Benny

doi:10.1145/3365265.3365273

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…To solve this, we have proposed a pose normalization method based on the PCA pose normalization in combination with the standard data deviation (PCA-STD). 38 To include the pose normalization preprocessing step into the recognition results, we first rotate each test data set object randomly along the threeaxis and later we normalize its pose using the PCA-STD method.…”

Section: Pose Normalizationmentioning

confidence: 99%

“…In experiment 4, we evaluated the pose normalization preprocessing step shown as the second operation in Figure 2. This pose normalization achieves rotational invariance for the 3DVHOG descriptor using the PCA-STD method defined by Vilar et al 38 Performance is evaluated in terms of averaged ACC Class of 20 different measurements as a key measurement. For the evaluation, we rotated all objects in the test data set randomly between 0 and 360 for the three axes.…”

Section: Experiments 4: Pose Normalizationmentioning

confidence: 99%

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Processing chain for 3D histogram of gradients based real-time object recognition

Vilar

Krug

Thörnberg

2021

International Journal of Advanced Robotic Systems

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3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

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Section: Pose Normalizationmentioning

confidence: 99%

Section: Experiments 4: Pose Normalizationmentioning

confidence: 99%

Processing chain for 3D histogram of gradients based real-time object recognition

Vilar

Krug

Thörnberg

2021

International Journal of Advanced Robotic Systems

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“…In experiment 2, we use a synthetic dataset for both training and classification data flows and additionally included the PCA-STD pose-normalization preprocessing [ 32 ] to achieve rotation invariance. The experimental goal was to verify its performance using a new synthetic dataset with respect previous research.…”

Section: Results and Analysismentioning

confidence: 99%

“… Pose alignment. In the same way as described for the training dataset, the segmented objects are preprocessed to normalize their pose using the PCA-STD method described in [ 32 ] in order to achieve rotational invariance. Voxelization.…”

Section: Methodsmentioning

confidence: 99%

“…The descriptor is based on a 3D voxel-based extension of the histogram-of-oriented-gradients (3DVHOG) [ 27 ] and was developed originally to detect hazard situations due to the presence of dangerous objects in a 3D scene. The descriptor parameters and feature dimensionality for each voxel grid analyzed are summarized in Table 2 and Table 3 and can be computed as is shown in Vilar et al [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

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Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Vilar

Krug

O’Nils

2021

Sensors

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3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.

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Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation

et al. 2021

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Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.

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Rotational Invariant Object Recognition for Robotic Vision

Cited by 3 publications

References 14 publications

Processing chain for 3D histogram of gradients based real-time object recognition

Processing chain for 3D histogram of gradients based real-time object recognition

Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation

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