Multi-object detection and behavior recognition from motion 3D data

2011 ◽  
Author(s):  
Kyungnam Kim ◽  
Michael Cao ◽  
Shankar Rao ◽  
Jiejun Xu ◽  
Swarup Medasani ◽  
...  
Keyword(s):  
Object Detection ◽  
Behavior Recognition ◽  
3D Data ◽  
And Behavior

scholarly journals A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2381
Author(s):  
Dan Li ◽  
Kaifeng Zhang ◽  
Zhenbo Li ◽  
Yifei Chen
Keyword(s):  
Network Architecture ◽  
Statistical Data ◽  
State Of The Art ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Test Set ◽  
Time Consumption ◽  
And Behavior ◽  
Human Eyes

The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig’s behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig’s multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.

scholarly journals USAGE OF MULTIPLE LIDAR SENSORS ON A MOBILE SYSTEM FOR THE DETECTION OF PERSONS WITH IMPLICIT SHAPE MODELS

2018 ◽  
Vol XLII-2 ◽  
pp. 125-131
Author(s):  
B. Borgmann ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Object Detection ◽  
Sensor Data ◽  
Mobile System ◽  
Data Merging ◽  
Shape Models ◽  
3D Data ◽  
Laser Scanners ◽  
Data Density ◽  
Direct Data ◽  
Sensor Information

The focus of this paper is the processing of data from multiple LiDAR (light detection and ranging) sensors for the purpose of detecting persons in that data. Many LiDAR sensors (e.g., laser scanners) use a rotating scan head, which makes it difficult to properly timesynchronize multiple of such LiDAR sensors. An improper synchronization between LiDAR sensors causes temporal distortion effects if their data are directly merged. A merging of data is desired, since it could increase the data density and the perceived area. For the usage in person and object detection tasks, we present an alternative which circumvents the problem by performing the merging of multi-sensor data in the voting space of a method that is based on Implicit Shape Models (ISM). Our approach already assumes that there exist some uncertainties in the voting space. Therefore it is robust against additional uncertainties induced by temporal distortions. Unlike many existing approaches for object detection in 3D data, our approach does not rely on a segmentation step in the data preprocessing. We show that our merging of multi-sensor information in voting space has its advantages in comparison to a direct data merging, especially in situations with a lot of distortion effects.

scholarly journals Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3594
Author(s):  
Hwiwon Lee ◽  
Sekyoung Youm
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Health Professionals ◽  
Medication Compliance ◽  
Image Data ◽  
Image Sensor ◽  
Training Dataset ◽  
Behavior Recognition ◽  
Recognition Model ◽  
Detection Model

As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals. The availability of prompt medication error reports can enable health professionals to provide immediate interventions for patients. Furthermore, it can enable clinical researchers to modify experiments easily and predict health levels based on medication compliance. This study proposes a method in which videos of patients taking medications are recorded using a camera image sensor integrated into a wearable device. The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique. As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that uses the combined feature values to perform action recognition (Model 2). Moreover, 50,000 image data were collected from 89 participants, and labeling was performed on different data categories to train the algorithm. The experimental combination of the object detection model (Model 1) and action recognition model (Model 2) was newly developed, and the accuracy was 92.7%, which is significantly high for medication behavior recognition. This study is expected to enable rapid intervention for providers seeking to treat patients through rapid reporting of drug errors.

Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness

2018 ◽  
Vol 57 (04) ◽  
pp. 1
Author(s):  
Mingliang Gao ◽  
Jun Jiang ◽  
Jin Shen ◽  
Guofeng Zou ◽  
Guixia Fu
Keyword(s):  
Motion Segmentation ◽  
Behavior Recognition ◽  
Crowd Motion ◽  
And Behavior

scholarly journals Tracking and climbing behavior recognition of heavy-duty trucks on roadways

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090362
Author(s):  
Lei Tang ◽  
Jingchi Jia ◽  
Zongtao Duan ◽  
Jingyu Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Driver Assistance System ◽  
Random Noise ◽  
Recognition Algorithm ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Heavy Duty ◽  
Driver Assistance ◽  
Behavior Recognition ◽  
And Behavior ◽  
Geographical Locations

The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.

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