Correlation filters (CFs) are well established and useful tools for a variety of tasks in signal processing and pattern recognition, including automatic target recognition and tracking, biometrics, landmark detection, and human action recognition. Traditionally, CFs have been designed and implemented efficiently in the frequency domain using the discrete Fourier transform (DFT). However, the element-wise multiplication of two DFTs in the frequency domain corresponds to a circular correlation, which results in aliasing (i.e., distortion) in the correlation output. Prior CF research has largely ignored these aliasing effects by making the assumption that linear correlation is approximated by circular correlation. In this work, we investigate in detail the topic of aliasing in CFs. First, we illustrate that the current formulation of CFs in the frequency domain is inherently flawed, as it unintentionally assumes circular correlation during the design phase. This means that existing CFs are not truly optimal. We introduce zero-aliasing correlation filters (ZACFs) which fix this formulation issue by ensuring that each CF formulation problem corresponds to a linear correlation rather than a circular correlation. By adopting the ZACF design modifications, we show that the recognition and localization performance of conventional CF designs can be significantly improved. We demonstrate these benefits using a variety of data sets and present solutions to the computational challenges associated with computing ZACFs. After a CF is designed, it is used for object recognition by correlating it with a test signal. We investigate the use of the well-known overlap-add (OLA) and overlap-save (OLS) algorithms to improve the computation and memory requirements of this correlation operation for high dimensional applications (e.g., video). Through this process, we highlight important tradeoffs between these two algorithms that have previously been undocumented. To improve the computation and memory requirements of OLA and OLS, we introduce a new block filtering scheme, denoted partial-aliasing OLA (PAOLA) that intentionally introduces aliasing into the output correlation. This aliasing causes conventional CFs to perform poorly. To remedy this, we introduce partial-aliasing correlation filters (PACFs), which are specifically designed to minimize this aliasing. We demonstrate through numerical results that PACFs outperform conventional CFs in the presence of aliasing.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-30T19:45:09Z No. of bitstreams: 1 dhiegocristianooliveiradasilvasad.pdf: 1920111 bytes, checksum: c7bccda6c65e798776738b9581721c98 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-01T11:37:10Z (GMT) No. of bitstreams: 1 dhiegocristianooliveiradasilvasad.pdf: 1920111 bytes, checksum: c7bccda6c65e798776738b9581721c98 (MD5) / Made available in DSpace on 2017-06-01T11:37:10Z (GMT). No. of bitstreams: 1 dhiegocristianooliveiradasilvasad.pdf: 1920111 bytes, checksum: c7bccda6c65e798776738b9581721c98 (MD5) Previous issue date: 2013-02-22 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho apresenta uma nova abordagem para a descrição de movimento em vídeos usando múltiplos filtros passa-banda que agem como estimadores derivativos de primeira ordem. A resposta dos filtros em cada quadro do vídeo é extraída e codificada em histogramas de gradientes para reduzir a sua dimensionalidade. Essa combinação é realizada através de tensores de orientação. O grande diferencial deste trabalho em relação à maioria das abordagens encontradas na literatura é que nenhuma característica local é extraída e nenhum método de aprendizagem é realizado previamente, isto é, o descritor depende unicamente do vídeo de entrada. Para o problema de reconhecimento da ação humana utilizando a base de dados KTH, nosso descritor alcançou a taxa de reconhecimento de 93,3% usando três filtros da família Daubechies combinado com mais um filtro extra que é a correlação entre esses três filtros. O descritor resultante é então classificado através do SVM utilizando um protocolo two-fold. Essa classificação se mostra superior para a maioria das abordagens que usam descritores globais e pode ser comparável aos métodos do estado-da-arte. / This work presents a novel approach for motion description in videos using multiple band-pass filters that act as first order derivative estimators. The filters response on each frame are coded into individual histograms of gradients to reduce their dimensionality. They are combined using orientation tensors. No local features are extracted and no learning is performed, i.e., the descriptor depends uniquely on the input video. Motion description can be enhanced even using multiple filters with similar or overlapping fre quency response. For the problem of human action recognition using the KTH database, our descriptor achieved the recognition rate of 93,3% using three Daubechies filters, one extra filter designed to correlate them, two-fold protocol and a SVM classifier. It is su perior to most global descriptor approaches and fairly comparable to the state-of-the-art methods.

Automatic tracking and video of surveillance on a farm could help to support farm management. In this project, an automated detection system is used to detect sows in surveillance videos. This system is based upon deep learning and computer vision methods. In order to minimize disk storage and to meet the network requirements necessary to achieve the real-performance, tracking in compressed video streams is essential. The proposed system uses a Discriminative Correlation Filter (DCF) as a classifier to detect targets. The tracking model is updated by training the classifier with online learning methods. Compression technology encodes the video data, thus reducing both the bit rates at which video signals are transmitted and helping the video transmission better adapt to the limited network bandwidth. However, compression may reduce the image quality of the videos the precision of our tracking may decrease. Hence, we conducted a performance evaluation of existing visual tracking algorithms on video sequences with quality degradation due to various compression parameters (encoders, target bitrate, rate control model, and Group of Pictures (GOP) size). The ultimate goal of video compression is to realize a tracking system with equal performance, but requiring fewer network resources. The proposed tracking algorithm successfully tracks each sow in consecutive frames in most cases. The performance of our tracker was benchmarked against two state-of-art tracking algorithms: Siamese Fully-Convolutional (FC) and Efficient Convolution Operators (ECO). The performance evaluation result shows our proposed tracker has similar performance to both Siamese FC and ECO. In comparison with the original tracker, the proposed tracker achieved similar tracking performance, while requiring much less storage and generating a lower bitrate when the video was compressed with appropriate parameters. However, the system is far slower than needed for real-time tracking due to high computational complexity; therefore, more optimal methods to update the tracking model will be needed to achieve real-time tracking. / Automatisk spårning av övervakning i gårdens område kan bidra till att stödja jordbruket management. I detta projekt till ett automatiserat system för upptäckt upptäcka suggor från övervaknings filmer kommer att utformas med djupa lärande och datorseende metoder. Av hänsyn till Diskhantering och tid och hastighet Krav över nätverket för att uppnå realtidsscenarier i framtiden är spårning i komprimerade videoströmmar är avgörande. Det föreslagna systemet i detta projekt skulle använda en DCF (diskriminerande korrelationsfilter) som en klassificerare att upptäcka mål. Spårningen modell kommer att uppdateras genom att utbilda klassificeraren med online inlärningsmetoder. Compression teknik kodar videodata och minskar bithastigheter där videosignaler sänds kan hjälpa videoöverföring anpassar bättre i begränsad nätverk. det kan dock reducera bildkvaliteten på videoklipp och leder exakt hastighet av vårt spårningssystem för att minska. Därför undersöker vi utvärderingen av prestanda av befintlig visuella spårningsalgoritmer på videosekvenser Det ultimata målet med videokomprimering är att bidra till att bygga ett spårningssystem med samma prestanda men kräver färre nätverksresurser. Den föreslagna spårning algoritm spår framgångsrikt varje sugga i konsekutiva ramar i de flesta fall prestanda vår tracker var jämföras med två state-of-art spårning algoritmer:. Siamese Fully-Convolutional (FC) och Efficient Convolution Operators (ECO) utvärdering av prestanda Resultatet visar vår föreslagna tracker blir liknande prestanda med Siamese FC och ECO. I jämförelse med den ursprungliga spårningen uppnådde den föreslagna spårningen liknande spårningseffektivitet, samtidigt som det krävde mycket mindre lagring och alstra en lägre bitrate när videon komprimerades med lämpliga parametrar. Systemet är mycket långsammare än det behövs för spårning i realtid på grund av hög beräkningskomplexitet; därför behövs mer optimala metoder för att uppdatera spårningsmodellen för att uppnå realtidsspårning.

Object tracking can be done in numerous ways, where the goal is to track a target through all frames in a sequence. The ground truth bounding box is used to initialize the object tracking algorithm. Object tracking can be carried out on infrared imagery suitable for military applications to execute tracking even without illumination. Objects, such as aircraft, can deploy countermeasures to impede tracking. The countermeasures most often mainly impact one wavelength band. Therefore, using two different wavelength bands for object tracking can counteract the impact of the countermeasures. The dataset was created from simulations. The countermeasures applied to the dataset are flares and Directional Infrared Countermeasures (DIRCMs). Different object tracking algorithms exist, and many are based on discriminative correlation filters (DCF). The thesis investigated the DCF-based trackers STRCF and ECO on the created dataset. The STRCF and the ECO trackers were analyzed using one and two wavelength bands. The following features were investigated for both trackers: grayscale, Histogram of Oriented Gradients (HOG), and pre-trained deep features. The results indicated that the STRCF and the ECO trackers using two wavelength bands instead of one improved performance on sequences with countermeasures. The use of HOG, deep features, or a combination of both improved the performance of the STRCF tracker using two wavelength bands. Likewise, the performance of the ECO tracker using two wavelength bands was improved by the use of deep features. However, the negative aspect of using two wavelength bands and introducing more features is that it resulted in a lower frame rate.