Visual object tracking via the local soft cosine similarity

Moujahid, Driss; Elharrouss, Omar; Tairi, Hamid

doi:10.1016/j.patrec.2018.03.026

Cited by 27 publications

(10 citation statements)

References 15 publications

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“…To evaluate the DTW distance and time series segmentation strategy in DTWS-ISD method separately, we proposed other three functions: ISD, Cos-ISD (improved ISD by Cosine similarity [ 40 , 41 ]) and DTW-ISD. The cosine value of the angle is also introduced to replace the Euclidean distance and improve the ISD function.…”

Section: Resultsmentioning

confidence: 99%

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Shi

Yongming

et al. 2018

Sensors

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For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.

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Section: Resultsmentioning

confidence: 99%

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Shi

Yongming

et al. 2018

Sensors

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“…Low lighting, uneven illumination or any change of illumination in the observed scene are among the sources of degradation that strongly affect video quality and consequently the process of scene analysis and understanding and particularly object detection and visual tracking performance [65,66]. It is therefore useful to detect the illumination changes and apply the appropriate pre-processing before performing high-level vision tasks such as moving object detection and tracking.…”

Section: Illumination Change Detection and Video Enhancementmentioning

confidence: 99%

A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

Self Cite

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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“…In the original contrastive loss paper [4], the distance d from equation 1 is the euclidean distance between vectors, but other related works show interest in learning a cosine similarity measure in the context of visual object tracking [27,18]; similarly, DeepSORT computes the cost matrix of data association using the cosine distance between vectors. Based on cosine similarity, we use the angular distance between vectors as our distance d: two closely related vectors in the embedding space should have an angular distance near 0, 1 otherwise.…”

Section: Detection Association With Siamese Cnnsmentioning

confidence: 99%

Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs

Labit-Bonis¹,

Thomas

Lerasle

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Knowing the exact number of passengers among the city bus fleets allows public transport operators to optimally distribute their vehicles into the traffic. However, interpreting overcrowded scenarios, at rush hour, with day/night illumination changes can be tricky. Based on the visual trackingby-detection paradigm, we benefit from video stream information provided by cameras placed above doors to infer people trajectories and deduce the number of enterings/leavings at every bus stop. In this way a person detector estimates the location of the passengers in each image, a tracker matches detections between successive frames based on different cues such as appearance or motion, and infers trajectories over time. This paper proposes a fast and embeddable framework that performs person detection using relevant state-of-the-art CNN detectors, and couple the best one (in our applicative context) with a newly designed Siamese network for real-time tracking/data association purposes. Evaluations on our own large scale in-situ dataset are very promising in terms of performances and real-time constraint expected for on-board processing.

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Visual object tracking via the local soft cosine similarity

Cited by 27 publications

References 15 publications

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

A combined multiple action recognition and summarization for surveillance video sequences

Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs

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