Mixture-Model-based Bounding Box Density Estimation for Object Detection

by   Jaeyoung Yoo, et al.
Seoul National University

In this paper, we propose a new object detection model, Mixture-Model-based Object Detector (MMOD), that performs multi-object detection using a mixture model. Unlike previous studies, we use density estimation to deal with the multi-object detection task. MMOD captures the conditional distribution of bounding boxes for a given input image using a mixture model consisting of Gaussian and categorical distributions. For this purpose, we propose a method to extract object bounding boxes from a trained mixture model. In doing so, we also propose a new network structure and objective function for the MMOD. Our proposed method is not trained by assigning a ground truth bounding box to a specific location on the network's output. Instead, the mixture components are automatically learned to represent the distribution of the bounding box through density estimation. Therefore, MMOD does not require a large number of anchors and does not incur the positive-negative imbalance problem. This not only benefits the detection performance but also enhances the inference speed without requiring additional processing. We applied MMOD to Pascal VOC and MS COCO datasets, and outperform the detection performance with inference speed of other state-of-the-art fast object detection methods. (38.7 AP with 39ms per image on MS COCO without bells and whistles.) Code will be available.


Sparse MDOD: Training End-to-End Multi-Object Detector without Bipartite Matching

Recent end-to-end multi-object detectors simplify the inference pipeline...

What and Where: A Context-based Recommendation System for Object Insertion

In this work, we propose a novel topic consisting of two dual tasks: 1) ...

Deep Multivariate Mixture of Gaussians for Object Detection under Occlusion

In this paper, we consider the problem of detecting object under occlusi...

Learning Instance-Aware Object Detection Using Determinantal Point Processes

Recent object detectors find instances while categorizing candidate regi...

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

We define the object detection from imagery problem as estimating a very...

Density Estimation for Geolocation via Convolutional Mixture Density Network

Nowadays, geographic information related to Twitter is crucially importa...

Mixture Dense Regression for Object Detection and Human Pose Estimation

Mixture models are well-established machine learning approaches that, in...

Please sign up or login with your details

Forgot password? Click here to reset