A Review of Video Object Detection: Datasets, Metrics and Methods

Zhu, Haidi; Wei, Haoran; Li, Baoqing; Yuan, Xia; Kehtarnavaz, Nasser

doi:10.3390/app10217834

Cited by 88 publications

(30 citation statements)

References 98 publications

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“…Such efforts are valuable especially for replacing network components by more compute-friendly counterparts. However, advantages of such techniques can also be limited due to natural trade-offs between speed and performance [50] as the lower-cost network components tend to have lower expressive power. Nevertheless, one can easily incorporate low-cost model design principles into DEQ or StreamDEQ models, thanks to the architecture-agnostic definition of implicit layer models.…”

Section: Efficient Video Processing and Inferencementioning

confidence: 99%

Streaming Multiscale Deep Equilibrium Models

Ertenli¹,

Akbaş²,

Cinbiş³

2022

Preprint

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We present StreamDEQ, a method that infers frame-wise representations on videos with minimal per-frame computation. In contrast to conventional methods where compute time grows at least linearly with the network depth, we aim to update the representations in a continuous manner. For this purpose, we leverage the recently emerging implicit layer model which infers the representation of an image by solving a fixed-point problem. Our main insight is to leverage the slowly changing nature of videos and use the previous frame representation as an initial condition on each frame. This scheme effectively recycles the recent inference computations and greatly reduces the needed processing time. Through extensive experimental analysis, we show that StreamDEQ is able to recover near-optimal representations in a few frames time, and maintain an up-to-date representation throughout the video duration. Our experiments on video semantic segmentation and video object detection show that StreamDEQ achieves on par accuracy with the baseline (standard MDEQ) while being more than 3× faster. The project page with example videos is available at: https://ufukertenli.github.io/streamdeq/ * Equal contribution for senior authorship.

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Section: Efficient Video Processing and Inferencementioning

confidence: 99%

Streaming Multiscale Deep Equilibrium Models

Ertenli¹,

Akbaş²,

Cinbiş³

2022

Preprint

View full text Add to dashboard Cite

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“…These days, object recognition from images and videos captured by digital cameras is being preferred by people for automated tasks related to security monitoring, public safety [2], pedestrian behavior analysis, etc. Different approaches for video object detection based on deep learning were studied in [3]. Pattern classification from images was also carried out in [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization

et al. 2021

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Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model.

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“…The simplest form of clustering is called partitional clustering, which is mainly to divide a given data set into disjoint clusters. In the partitional clustering problem, each cluster with approximate similar points is a very common scenario, such as image segmentation [8] and object tracking [9] and movement detection [10], [11]. In order to solve this problem, many clustering algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated Faster Mean Shift with euclidean distance metrics

You¹,

Han²,

Hu³

et al. 2021

Preprint

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Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the meanshift algorithm is restricted by its huge computational resource cost. In previous research [1], we proposed a novel GPUaccelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based meanshift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed.

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A Review of Video Object Detection: Datasets, Metrics and Methods

Cited by 88 publications

References 98 publications

Streaming Multiscale Deep Equilibrium Models

Streaming Multiscale Deep Equilibrium Models

Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization

GPU-accelerated Faster Mean Shift with euclidean distance metrics

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