STDnet: Exploiting high resolution feature maps for small object detection

The accuracy of small object detection with convolutional neural networks (ConvNets) lags behind that of larger objects. This can be observed in popular contests like MS COCO. This is in part caused by the lack of specific architectures and datasets with a sufficiently large number of small objects. Our work aims at these two issues. First, this paper introduces STDnet, a convolutional neural network focused on the detection of small objects that we defined as those under 16 × 16 pixels. The high performance of STDnet is built on a novel early visual attention mechanism, called Region Context Network (RCN), to choose the most promising regions, while discarding the rest of the input image. Processing only specific areas allows STDnet to keep high resolution feature maps in deeper layers providing low memory overhead and higher frame rates. High resolution feature maps were proved to be key to increasing localization accuracy in such small objects. Second, we also present USC-GRAD-STDdb, a video dataset with more than 56,000 annotated small objects in challenging scenarios. Experimental results over USC-GRAD-STDdb show that STDnet improves the [email protected] of the best state-of-the-art object detectors for small target detection from 50.8% to 57.4%. Performance has also been tested in MS COCO for objects under 16 × 16 pixels. In addition, a spatio-temporal baseline network, STDnet-bST, has been proposed to make use of the information of successive frames, increasing the [email protected] of STDnet in 2.3%. Finally, optimizations have been carried out to be fit on embedded devices such as Jetson TX2This research was funded by Gradiant, Spain, and also partially funded by the Spanish Ministry of Economy and Competitiveness under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32 (MICINN), and the Galician Ministry of Education, Culture and Universities, Spain under grant ED431G/08. Brais Bosquet is supported by the Galician Ministry of Education, Culture and Universities, Spain . These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Real-time siamese multiple object tracker with enhanced proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://www.github.com/lorenzovaquero/SiamMOTIONThis research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2021/048, ED431G 2019/04]. These grants are co-funded by the European Regional Development Fund (ERDF). Lorenzo Vaquero is supported by the Spanish Ministerio de Universidades under the FPU national plan (FPU18/03174). We also gratefully acknowledge the support of NVIDIA Corporation for hardware donations used for this researchS

Tracking more than 100 arbitrary objects at 25 FPS through deep learning

Most video analytics applications rely on object detectors to localize objects in frames. However, when real-time is a requirement, running the detector at all the frames is usually not possible. This is somewhat circumvented by instantiating visual object trackers between detector calls, but this does not scale with the number of objects. To tackle this problem, we present SiamMT, a new deep learning multiple visual object tracking solution that applies single-object tracking principles to multiple arbitrary objects in real-time. To achieve this, SiamMT reuses feature computations, implements a novel crop-and-resize operator, and defines a new and efficient pairwise similarity operator. SiamMT naturally scales up to several dozens of targets, reaching 25 fps with 122 simultaneous objects for VGA videos, or up to 100 simultaneous objects in HD720 video. SiamMT has been validated on five large real-time benchmarks, achieving leading performance against current state-of-the-art trackersThis research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2017/69, accreditation 2016–2019, ED431G/08]. These grants are co-funded by the European Regional Development Fund (ERDF). Lorenzo Vaquero is supported by the Spanish Ministerio de Universidades under the FPU national plan (FPU18/03174)S

PRECISION: A Reconfigurable SIMD/MIMD Coprocessor for Computer Vision Systems-on-Chip

Computer vision applications have a large disparity in operations, data representation and memory access patterns from the early vision stages to the final classification and recognition stages. A hardware system for computer vision has to provide high flexibility without compromising performance, exploiting massively spatial-parallel operations but also keeping a high throughput on data-dependent and complex program flows. Furthermore, the architecture must be modular, scalable and easy to adapt to the needs of different applications. Keeping this in mind, a hybrid SIMD/MIMD architecture for embedded computer vision is proposed. It consists of a coprocessor designed to provide fast and flexible computation of demanding image processing tasks of vision applications. A 32-bit 128-unit device was prototyped on a Virtex-6 FPGA which delivers a peak performance of 19.6 GOP/s and 7.2 W of power dissipationThis work is funded by the Ministry of Science and Innovation, Government of Spain (projects TIN2013-41129-P and TEC2012-38921-C02-02) and the Xunta de Galicia (contract GRC 2014/008)S

Short-term anchor linking and long-term self-guided attention for video object detection

We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal information. This module takes advantage of geometrical features in the long-term for the first time in the video object detection domain. Finally, a spatio-temporal double head is fed with both spatial information from the reference frame and the aggregated information that takes into account the short- and long-term temporal context. We have tested our proposal in five video object detection datasets with very different characteristics, in order to prove its robustness in a wide number of scenarios. Non-parametric statistical tests show that our approach outperforms the state-of-the-art. Our code is available at https://github.com/daniel-cores/SLTnetThis research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

Real-time visual detection and tracking system for traffic monitoring

Computer vision systems for traffic monitoring represent an essential tool for a broad range of traffic surveillance applications. Two of the most noteworthy challenges for these systems are the real-time operation with hundreds of vehicles and the total occlusions which hinder the tracking of the vehicles. In this paper, we present a traffic monitoring approach that deals with these two challenges based on three modules: detection, tracking and data association. First, vehicles are identified through a deep learning based detector. Second, tracking is performed with a combination of a Discriminative Correlation Filter and a Kalman Filter. This permits to estimate the tracking error in order to make tracking more robust and reliable. Finally, the data association through the Hungarian algorithm combines the information of the previous steps. The contributions are: (i) a real-time traffic monitoring system robust to occlusions that can process more than four hundred vehicles simultaneously; and (ii) the application of the system to anomaly detection in traffic and roundabout input/output analysis. The system has been evaluated with more than two thousand vehicles in real-life videosThis research was partially funded by the Spanish Ministry of Science and Innovation under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grant ED431G/08. Mauro Fernández is supported by the Spanish Ministry of Economy and Competitiveness under grant BES-2015-071889. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)S

CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

Offset-compensated comparator with full-input range in 150nm FDSOI CMOS-3d technology

This paper addresses an offset-compensated comparator with full-input range in the 150nm FDSOI CMOS- 3D technology from MIT- Lincoln Laboratory. The comparator discussed here makes part of a vision system. Its architecture is that of a self-biased inverter with dynamic offset correction. At simulation level, the comparator can reach a resolution of 0.1mV in an area of approximately 220μm2 with a time response of less than 40ns and a static power dissipation of 1.125μW

In-pixel generation of gaussian pyramid images by block reusing in 3D-CMOS

This paper introduces an architecture of a switched-capacitor network for Gaussian pyramid generation. Gaussian pyramids are used in modern scale- and rotation-invariant feature detectors or in visual attention. Our switched-capacitor architecture is conceived within the framework of a CMOS-3D-based vision system. As such, it is also used during the acquisition phase to perform analog storage and Correlated Double Sampling (CDS). The paper addresses mismatch, and switching errors like feedthrough and charge injection. The paper also gives an estimate of the area occupied by each pixel on the 130nm CMOS-3D technology by Tezzaron. The validity of our proposal is assessed through object detection in a scale- and rotation-invariant feature detector.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686Office of Naval Research (USA) N00014111031

A full data augmentation pipeline for small object detection based on generative adversarial networks

Object detection accuracy on small objects, i.e., objects under 32 32 pixels, lags behind that of large ones. To address this issue, innovative architectures have been designed and new datasets have been released. Still, the number of small objects in many datasets does not suffice for training. The advent of the generative adversarial networks (GANs) opens up a new data augmentation possibility for training architectures without the costly task of annotating huge datasets for small objects. In this paper, we propose a full pipeline for data augmentation for small object detection which combines a GAN-based object generator with techniques of object segmentation, image inpainting, and image blending to achieve high-quality synthetic data. The main component of our pipeline is DS-GAN, a novel GAN-based architecture that generates realistic small objects from larger ones. Experimental results show that our overall data augmentation method improves the performance of state-of-the-art models up to 11.9% AP on UAVDT and by 4.7% AP on iSAID, both for the small objects subset and for a scenario where the number of training instances is limited.This research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2021/048, ED431G 2019/04]. These grants are co-funded by the European Regional Development Fund (ERDF). This paper was supported by European Union’s Horizon 2020 research and innovation programme under grant numberS