168,345 research outputs found
Particle Filter with Integrated Multiple Features for Object Detection and Tracking
Considering objects in the environments (or scenes), object detection is the first task needed to be accomplished to recognize those objects. There are two problems needed to be considered in object detection. First, a single feature based object detection is difficult regarding types of the objects and scenes. For example, object detection that is based on color information will fail in the dark place. The second problem is the object’s pose in the scene that is arbitrary in general. This paper aims to tackle such problems for enabling the object detection and tracking of various types of objects in the various scenes. This study proposes a method for object detection and tracking by using a particle filter and multiple features consisting of color, texture, and depth information that are integrated by adaptive weights. To validate the proposed method, the experiments have been conducted. The results revealed that the proposed method outperformed the previous method, which is based only on color information
Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking
The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps.
This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks.
Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets.
For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold.
Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data.
Adivser: Lance C. Pére
Automated Driving Systems Data Acquisition and Processing Platform
This paper presents an automated driving system (ADS) data acquisition and
processing platform for vehicle trajectory extraction, reconstruction, and
evaluation based on connected automated vehicle (CAV) cooperative perception.
This platform presents a holistic pipeline from the raw advanced sensory data
collection to data processing, which can process the sensor data from multiple
CAVs and extract the objects' Identity (ID) number, position, speed, and
orientation information in the map and Frenet coordinates. First, the ADS data
acquisition and analytics platform are presented. Specifically, the
experimental CAVs platform and sensor configuration are shown, and the
processing software, including a deep-learning-based object detection algorithm
using LiDAR information, a late fusion scheme to leverage cooperative
perception to fuse the detected objects from multiple CAVs, and a multi-object
tracking method is introduced. To further enhance the object detection and
tracking results, high definition maps consisting of point cloud and vector
maps are generated and forwarded to a world model to filter out the objects off
the road and extract the objects' coordinates in Frenet coordinates and the
lane information. In addition, a post-processing method is proposed to refine
trajectories from the object tracking algorithms. Aiming to tackle the ID
switch issue of the object tracking algorithm, a fuzzy-logic-based approach is
proposed to detect the discontinuous trajectories of the same object. Finally,
results, including object detection and tracking and a late fusion scheme, are
presented, and the post-processing algorithm's improvements in noise level and
outlier removal are discussed, confirming the functionality and effectiveness
of the proposed holistic data collection and processing platform
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